图像处理-毕设论文外文翻译(翻译+原文)
图像处理中值滤波器中英文对照外文翻译文献
中英文资料对照外文翻译一、英文原文A NEW CONTENT BASED MEDIAN FILTERABSTRACTIn this paper the hardware implementation of a contentbased median filter suitabl e for real-time impulse noise suppression is presented. The function of the proposed ci rcuitry is adaptive; it detects the existence of impulse noise in an image neighborhood and applies the median filter operator only when necessary. In this way, the blurring o f the imagein process is avoided and the integrity of edge and detail information is pre served. The proposed digital hardware structure is capable of processing gray-scale im ages of 8-bit resolution and is fully pipelined, whereas parallel processing is used to m inimize computational time. The architecturepresented was implemented in FPGA an d it can be used in industrial imaging applications, where fast processing is of the utm ost importance. The typical system clock frequency is 55 MHz.1. INTRODUCTIONTwo applications of great importance in the area of image processing are noise filtering and image enhancement [1].These tasks are an essential part of any image pro cessor,whether the final image is utilized for visual interpretation or for automatic an alysis. The aim of noise filtering is to eliminate noise and its effects on the original im age, while corrupting the image as little as possible. To this end, nonlinear techniques (like the median and, in general, order statistics filters) have been found to provide mo re satisfactory results in comparison to linear methods. Impulse noise exists in many p ractical applications and can be generated by various sources, including a number of man made phenomena, such as unprotected switches, industrial machines and car ign ition systems. Images are often corrupted by impulse noise due to a noisy sensor or ch annel transmission errors. The most common method used for impulse noise suppressi on n forgray-scale and color images is the median filter (MF) [2].The basic drawback o f the application of the MF is the blurringof the image in process. In the general case,t he filter is applied uniformly across an image, modifying pixels that arenot contamina ted by noise. In this way, the effective elimination of impulse noise is often at the exp ense of an overalldegradation of the image and blurred or distorted features[3].In this paper an intelligent hardware structure of a content based median filter (CBMF) suita ble for impulse noise suppression is presented. The function of the proposed circuit is to detect the existence of noise in the image window and apply the corresponding MFonly when necessary. The noise detection procedure is based on the content of the im age and computes the differences between the central pixel and thesurrounding pixels of a neighborhood. The main advantage of this adaptive approach is that image blurrin g is avoided and the integrity of edge and detail information are preserved[4,5]. The pro posed digital hardware structure is capable of processing gray-scale images of 8-bitres olution and performs both positive and negative impulse noise removal. The architectt ure chosen is based on a sequence of four basic functional pipelined stages, and parall el processing is used within each stage. A moving window of a 3×3 and 5×5-pixel im age neighborhood can be selected. However, the system can be easily expanded to acc ommodate windows of larger sizes. The proposed structure was implemented using fi eld programmable gate arrays (FPGA). The digital circuit was designed, compiled and successfully simulated using the MAX+PLUS II Programmable Logic Development S ystem by Altera Corporation. The EPF10K200SFC484-1 FPGA device of the FLEX1 0KE device family was utilized for the realization of the system. The typical clock fre quency is 55 MHz and the system can be used for real-time imaging applications whe re fast processing is required [6]. As an example,the time required to perform filtering of a gray-scale image of 260×244 pixels is approximately 10.6 msec.2. ADAPTIVE FILTERING PROCEDUREThe output of a median filter at a point x of an image f depends on the values of t he image points in the neighborhood of x. This neighborhood is determined by a wind ow W that is located at point x of f including n points x1, x2, …, xn of f, with n=2k+1. The proposed adaptive content based median filter can be utilized for impulse noisesu p pression in gray-scale images. A block diagram of the adaptive filtering procedure is depicted in Fig. 1. The noise detection procedure for both positive and negative noise is as follows:(i) We consider a neighborhood window W that is located at point x of the image f. Th e differences between the central pixel at point x and the pixel values of the n-1surr ounding points of the neighborhood (excluding thevalue of the central pixel) are co mputed.(ii) The sum of the absolute values of these differences is computed, denoted as fabs(x ). This value provides ameasure of closeness between the central pixel and its su rrounding pixels.(iii) The value fabs(x) is compared to fthreshold(x), which is anappropriately selected positive integer threshold value and can be modified. The central pixel is conside red to be noise when the value fabs(x) is greater than thethreshold value fthresho d(x).(iv) When the central pixel is considered to be noise it is substituted by the median val ue of the image neighborhood,denoted as fk+1, which is the normal operationof the median filter. In the opposite case, the value of the central pixel is not altered and the procedure is repeated for the next neighborhood window.From the noised etection scheme described, it should be mentioned that the noise detection level procedure can be controlled and a range of pixel values (and not only the fixedvalues of 0 and 255, salt and pepper noise) is considered asimpulse noise.In Fig. 2 the results of the application of the median filter and the CBMF in the gray-sca le image “Peppers” are depicted.More specifically, in Fig. 2(a) the original,uncor rupted image“Peppers” is depicted. In Fig. 2(b) the original imagedegraded by 5% both positive and negative impulse noise isillustrated. In Figs 2(c) and 2(d) the resultant images of the application of median filter and CBMF for a 3×3-pixel win dow are shown, respectively. Finally, the resultant images of the application of m edian filter and CBMF for a 5×5-pixelwindow are presented in Figs 2(e) and 2(f). It can be noticed that the application of the CBMF preserves much better edges a nddetails of the images, in comparison to the median filter.A number of different objective measures can be utilized forthe evaluation of these results. The most wi dely used measures are the Mean Square Error (MSE) and the Normalized Mean Square Error (NMSE) [1]. The results of the estimation of these measures for the two filters are depicted in Table I.For the estimation of these measures, the result ant images of the filters are compared to the original, uncorrupted image.From T able I it can be noticed that the MSE and NMSE estimatedfor the application of t he CBMF are considerably smaller than those estimated for the median filter, in all the cases.Table I. Similarity measures.3. HARDWARE ARCHITECTUREThe structure of the adaptive filter comprises four basic functional units, the mo ving window unit , the median computation unit , the arithmetic operations unit , and th e output selection unit . The input data of the system are the gray-scale values of the pi xels of the image neighborhood and the noise threshold value. For the computation of the filter output a3×3 or 5×5-pixel image neighborhood can be selected. Image input d ata is serially imported into the first stage. In this way,the total number of the inputpin s are 24 (21 inputs for the input data and 3 inputs for the clock and the control signalsr equired). The output data of the system are the resultant gray-scale values computed f or the operation selected (8pins).The moving window unit is the internal memory of the system,used for storing th e input values of the pixels and for realizing the moving window operation. The pixel values of the input image, denoted as “IMAGE_INPUT[7..0]”, areimported into this u nit in serial. For the representation of thethreshold value used for the detection of a no Filter Impulse noise 5% mse Nmse(×10-2) 3×3 5×5 3×3 5×5Median CBMF 57.554 35.287 130.496 84.788 0.317 0.194 0.718 0.467ise pixel 13 bits are required. For the moving window operation a 3×3 (5×5)-pixel sep entine type memory is used, consisting of 9 (25)registers. In this way,when the windoP1 P2 P3w is moved into the next image neighborhood only 3 or 5 pixel values stored in the memory are altered. The “en5×5” control signal is used for the selection of the size of th e image window, when“en5×5” is equal to “0” (“1”) a 3×3 (5×5)-pixel neighborhood is selected. It should be mentioned that the modules of the circuit used for the 3×3-pix el window are utilized for the 5×5-pixel window as well. For these modules, 2-to-1mu ltiplexers are utilized to select the appropriate pixel values,where necessary. The mod ules that are utilized only in the case of the 5×5-pixel neighborhood are enabled by th e“en5×5” control signal. The outputs of this unit are rows ofpixel values (3 or 5, respe ctively), which are the inputs to the median computation unit.The task of the median c omputation unit is to compute themedian value of the image neighborhood in order to substitutethe central pixel value, if necessary. For this purpose a25-input sorter is utili zeed. The structure of the sorter has been proposed by Batcher and is based on the use of CS blocks. ACS block is a max/min module; its first output is the maximumof the i nputs and its second output the minimum. The implementation of a CS block includes a comparator and two 2-to-1 multiplexers. The outputs values of the sorter, denoted a s “OUT_0[7..0]”…. “OUT_24[7..0]”, produce a “sorted list” of the 25 initial pixel val ues. A 2-to-1 multiplexer isused for the selection of the median value for a 3×3 or 5×5-pixel neighborhood.The function of the arithmetic operations unit is to computethe value fabs(x), whi ch is compared to the noise threshold value in the final stage of the adaptive filter.The in puts of this unit are the surrounding pixel values and the central pixelof the neighb orhood. For the implementation of the mathematical expression of fabs(x), the circuit of this unit contains a number of adder modules. Note that registers have been used to achieve a pipelined operation. An additional 2-to-1 multiplexer is utilized for the selec tion of the appropriate output value, depending on the “en5×5” control signal. From th e implementation point of view, the use of arithmetic blocks makes this stage hardwar e demanding.The output selection unit is used for the selection of the appropriateoutput value of the performed noise suppression operation. For this selection, the corresponding no ise threshold value calculated for the image neighborhood,“NOISE_THRES HOLD[1 2..0]”,is employed. This value is compared to fabs(x) and the result of the comparison Classifies the central pixel either as impulse noise or not. If thevalue fabs(x) is greater than the threshold value fthreshold(x) the central pixel is positive or negative impulse noise and has to be eliminated. For this reason, the output of the comparison is used as the selection signal of a 2-to-1 multiplexer whose inputs are the central pixel and the c orresponding median value for the image neighborhood. The output of the multiplexer is the output of this stage and the final output of the circuit of the adaptive filter.The st ructure of the CBMF, the computation procedure and the design of the four aforeme n tioned units are illustrated in Fig. 3.ImagewindoeFigure 1: Block diagram of the filtering methodFigure 2: Results of the application of the CBMF: (a) Original image, (b) noise corrupted image (c) Restored image by a 3x3 MF, (d) Restored image by a 3x3 CBMF, (e) Restored image by a 5x5 MF and (f) Restored image by a 5x5 CBMF.4. IMPLEMENTATION ISSUESThe proposed structure was implemented in FPGA,which offer an attractive com bination of low cost, high performance and apparent flexibility, using the software pa ckage+PLUS II of Altera Corporation. The FPGA used is the EPF10K200SFC484-1 d evice of the FLEX10KE device family,a device family suitable for designs that requir e high densities and high I/O count. The 99% of the logic cells(9965/9984 logic cells) of the device was utilized to implement the circuit . The typical operating clock frequ ency of the system is 55 MHz. As a comparison, the time required to perform filtering of a gray-scale image of 260×244 pixelsusing Matlab® software on a Pentium 4/2.4 G Hz computer system is approximately 7.2 sec, whereas the corresponding time using h ardware is approximately 10.6 msec.The modification of the system to accommodate windows oflarger sizes can be done in a straightforward way, requiring onlya small nu mber of changes. More specifically, in the first unit the size of the serpentine memory P4P5P6P7P8P9SubtractorarryMedianfilteradder comparatormuitiplexerf abc(x)valueand the corresponding number of multiplexers increase following a square law. In the second unit, the sorter module should be modified,and in the third unit the number of the adder devicesincreases following a square law. In the last unit no changes are requ ired.5. CONCLUSIONSThis paper presents a new hardware structure of a content based median filter, ca pable of performing adaptive impulse noise removal for gray-scale images. The noise detection procedure takes into account the differences between the central pixel and th e surrounding pixels of a neighborhood.The proposed digital circuit is capable ofproce ssing grayscale images of 8-bit resolution, with 3×3 or 5×5-pixel neighborhoods as op tions for the computation of the filter output. However, the design of the circuit is dire ctly expandableto accommodate larger size image windows. The adaptive filter was d eigned and implemented in FPGA. The typical clock frequency is 55 MHz and the sys tem is suitable forreal-time imaging applications.REFERENCES[1] W. K. Pratt, Digital Image Processing. New York: Wiley,1991.[2] G. R. Arce, N. C. Gallagher and T. Nodes, “Median filters:Theory and applicat ions,” in Advances in ComputerVision and Image Processing, Greenwich, CT: JAI, 1986.[3] T. A. Nodes and N. C. Gallagher, Jr., “The output distributionof median type filte rs,” IEEE Transactions onCommunications, vol. COM-32, pp. 532-541, May1984.[4] T. Sun and Y. Neuvo, “Detail-preserving median basedfilters in imageprocessing,” Pattern Recognition Letters,vol. 15, pp. 341-347, Apr. 1994.[5] E. Abreau, M. Lightstone, S. K. Mitra, and K. Arakawa,“A new efficient approachfor the removal of impulsenoise from highly corrupted images,” IEEE Transa ctionson Image Processing, vol. 5, pp. 1012-1025, June 1996.[6] E. R. Dougherty and P. Laplante, Introduction to Real-Time Imaging, Bellingham:SPIE/IEEE Press, 1995.二、英文翻译基于中值滤波的新的内容摘要在本设计中的提出了基于中值滤波的硬件实现用来抑制脉冲噪声的干扰。
图像处理毕业论文
毕业论文（设计）题目：数字图像处理系统的设计与实现姓名：学院：理学与信息科学学院专业：计算机科学与技术班级：学号：指导教师：完成时间：数字图像处理系统的设计与实现摘要：随着信息技术的蓬勃发展，尤其是计算机技术的日新月异，为数字图像处理的发展提供了广阔的空间。
该数字图像处理系统是基于Windows平台的图像处理系统，实现了对灰度级图像的编辑，可以进行图像导入和导出，视图设置，可以调整图片尺寸，旋转和翻转图片，图片增强优化，图像边缘检测与分割，图像编码以及打印输出图片。
本文主要介绍了数字图像处理系统的设计和实现过程，系统设计运用MFC的设计思想，通过VC++实现系统框架，简化了软件的开发，提高了软件系统的灵活性、可扩展性和重用性。
同时系统所有的操作设计得十分简单方便，无需具备有专业的知识，也能对图片完成编辑操作。
关键词：VC++；MFC；灰度级图像；图像编辑The Design and Implementation of Digital Image Processing SystemAbstract：With the rapid development of information technology, especially in the progress of computer technology, it provides wide space to the application of Digital Image Processing. Digital image processing system is an image processing system based on the Windows platform. To realize the image editor of gray level, import and export images, view settings, you can adjust picture size, rotate and flip images Enhance the optimization and print output picture.The analysis and the implementation procedure of Digital Image Processing System were introduced in this paper. The design idea of MFC was used and the system structure was implemented by VC++. So the development of software can be predigested and flexibility, expansibility and reusability of software system can be improved.Keywords: VC++; MFC; Grayscale image; Image edit目录前言 (1)1 概述 (2)1.1课题设计的背景和意义 (2)1.2数字图像处理的方法概要与应用领域 (2)1.2.1 数字图像处理的方法概要 (2)1.2.2数字图像处理的应用领域 (4)1.3数字图像系统简介 (5)2 数字图像处理系统开发技术基础 (6)2.1C++语言优点 (6)2.2VC++平台简介 (7)2.3MFC技术简介 (8)2.3.1 封装 (8)2.3.2继承 (9)2.3.3虚拟函数和动态约束 (9)2.4MDI应用程序的构成 (10)3 需求分析 (12)3.1系统功能需求分析 (12)3.2系统处理流程分析 (12)4 系统总体设计 (14)4.1系统功能模块划分 (14)4.2类的设计 (15)4.2.1对话框类 (15)4.2.2 CMyDIB、CBmpShow、CRectTrackerEx类 (15)4.2.3系统框架类 (15)5 系统的详细设计 (16)5.1文件模块的设计 (16)5.2图像编辑模块 (18)5.3图像处理模块 (19)5.3.1图像的点运算 (20)5.3.2图像的几何运算 (23)5.3.3图像的正交变换 (25)5.3.4图像的增强和复原 (26)5.3.5图像边缘检测与分割 (28)5.3.6图像编码 (31)5.2系统调试 (32)结束语 (34)致谢 (35)参考文献 (36)前言数字图像处理（Digital Image Processing）又称为计算机图像处理，它是指将图像信号转换成数字信号并利用计算机对其进行处理的过程。
图像识别中英文对照外文翻译文献
中英文对照外文翻译文献(文档含英文原文和中文翻译)Elastic image matchingAbstractOne fundamental problem in image recognition is to establish the resemblance of two images. This can be done by searching the best pixel to pixel mapping taking into account monotonicity and continuity constraints. We show that this problem is NP-complete by reduction from 3-SAT, thus giving evidence that the known exponential time algorithms are justified, but approximation algorithms or simplifications are necessary.Keywords: Elastic image matching; Two-dimensional warping; NP-completeness 1. IntroductionIn image recognition, a common problem is to match two given images, e.g. when comparing an observed image to given references. In that pro-cess, elastic image matching, two-dimensional (2D-)warping (Uchida and Sakoe, 1998) or similar types of invariant methods (Keysers et al., 2000) can be used. For this purpose, we can define cost functions depending on the distortion introduced in the matching andsearch for the best matching with respect to a given cost function. In this paper, we show that it is an algorithmically hard problem to decide whether a matching between two images exists with costs below a given threshold. We show that the problem image matching is NP-complete by means of a reduction from 3-SAT, which is a common method of demonstrating a problem to be intrinsically hard (Garey and Johnson, 1979). This result shows the inherent computational difficulties in this type of image comparison, while interestingly the same problem is solvable for 1D sequences in polynomial time, e.g. the dynamic time warping problem in speech recognition (see e.g. Ney et al., 1992). This has the following implications: researchers who are interested in an exact solution to this problem cannot hope to find a polynomial time algorithm, unless P=NP. Furthermore, one can conclude that exponential time algorithms as presented and extended by Uchida and Sakoe (1998, 1999a,b, 2000a,b) may be justified for some image matching applications. On the other hand this shows that those interested in faster algorithms––e.g. for pattern recognition purposes––are right in searching for sub-optimal solutions. One method to do this is the restriction to local optimizations or linear approximations of global transformations as presented in (Keysers et al., 2000). Another possibility is to use heuristic approaches like simulated annealing or genetic algorithms to find an approximate solution. Furthermore, methods like beam search are promising candidates, as these are used successfully in speech recognition, although linguistic decoding is also an NP-complete problem (Casacuberta and de la Higuera, 1999). 2. Image matchingAmong the varieties of matching algorithms,we choose the one presented by Uchida and Sakoe(1998) as a starting point to formalize the problem image matching. Let the images be given as(without loss of generality) square grids of size M×M with gray values (respectively node labels)from a finite alphabet &={1，…，G}. To define thed：&×&→N , problem, two distance functions are needed,one acting on gray valuesg measuring the match in gray values, and one acting on displacement differences :Z×Z→N , measuring the distortion introduced by t he matching. For these distance ddfunctions we assume that they are monotonous functions (computable in polynomial time) of the commonly used squared Euclid-ean distance, i.ed g (g 1,g 2)=f 1(||g 1-g 2||²)and d d (z)=f 2(||z||²) monotonously increasing. Now we call the following optimization problem the image matching problem (let µ={1，…M} ).Instance: The pair( A ; B ) of two images A and B of size M×M .Solution: A mapping function f :µ×µ→µ×µ.Measure:c （A,B,f ）=),(),(j i f ij g B Ad ∑μμ⨯∈),(j i+∑⨯-⋅⋅⋅∈+-+μ}1,{1,),()))0,1(),(())0,1(),(((M j i d j i f j i f dμ⨯-⋅⋅⋅∈}1,{1,),(M j i +∑⋅⋅⋅⨯∈+-+1}-M ,{1,),()))1,0(),(())1,0(),(((μj i d j i f j i f d 1}-M ,{1,),(⋅⋅⋅⨯∈μj iGoal:min f c(A,B,f).In other words, the problem is to find the mapping from A onto B that minimizes the distance between the mapped gray values together with a measure for the distortion introduced by the mapping. Here, the distortion is measured by the deviation from the identity mapping in the two dimensions. The identity mapping fulfills f(i,j)=(i,j),and therefore ，f((i,j)+(x,y))=f(i,j)+(x,y)The corresponding decision problem is fixed by the followingQuestion:Given an instance of image matching and a cost c′, does there exist a ma pping f such that c(A,B,f)≤c′?In the definition of the problem some care must be taken concerning the distance functions. For example, if either one of the distance functions is a constant function, the problem is clearly in P (for d g constant, the minimum is given by the identity mapping and for d d constant, the minimum can be determined by sorting all possible matching for each pixel by gray value cost and mapping to one of the pixels with minimum cost). But these special cases are not those we are concerned with in image matching in general.We choose the matching problem of Uchida and Sakoe (1998) to complete the definition of the problem. Here, the mapping functions are restricted by continuity and monotonicity constraints: the deviations from the identity mapping may locally be at most one pixel (i.e. limited to the eight-neighborhood with squared Euclidean distance less than or equal to 2). This can be formalized in this approach bychoosing the functions f1,f2as e.g.f 1=id，f2(x)=step(x):=⎩⎨⎧.2,)10(,2,0>≤⋅xGxMM3. Reduction from 3-SAT3-SAT is a very well-known NP-complete problem (Garey and Johnson, 1979), where 3-SAT is defined as follows:Instance: Collection of clauses C={C1，···,CK} on a set of variables X={x1， (x)L}such that each ckconsists of 3 literals for k=1，···K .Each literal is a variable or the negation of a variable.Question:Is there a truth assignment for X which satisfies each clause ck, k=1，···K ?The dependency graph D（Ф）corresponding to an instance Ф of 3-SAT is defined to be the bipartite graph whose independent sets are formed by the set of clauses Cand the set of variables X .Two vert ices ck and x1are adjacent iff ckinvolvesx 1or-xL.Given any 3-SAT formula U, we show how to construct in polynomial time anequivalent image matching problem l（Ф）=（A(Ф),B(Ф)）; . The two images of l （Ф）are similar according to the cost function （i.e.f:c(A(Ф),B(Ф),f）≤0) iff the formulaФ is satisfiable. We perform the reduction from 3-SAT using the following steps:• From the formula Ф we construct the dependency graph D（Ф）.• The dependency graph D（Ф）is drawn in the plane.• The drawing of D（Ф）is refined to depict the logical behaviour of Ф , yielding two images（A(Ф),B(Ф)）.For this, we use three types of components: one component to represent variables of Ф , one component to represent clauses of Ф, and components which act as interfaces between the former two types. Before we give the formal reduction, we introduce these components.3.1. Basic componentsFor the reduction from 3-SAT we need five components from which we will construct the in-stances for image matching , given a Boolean formula in 3-DNF,respectively its graph. The five components are the building blocks needed for the graph drawing and will be introduced in the following, namely the representations of connectors,crossings, variables, and clauses. The connectors represent the edges and have two varieties, straight connectors and corner connectors. Each of the components consists of two parts, one for image A and one for image B , where blank pixels are considered to be of the‘background ’color.We will depict possible mappings in the following using arrows indicating the direction of displacement (where displacements within the eight-neighborhood of a pixel are the only cases considered). Blank squares represent mapping to the respective counterpart in the second image.For example, the following displacements of neighboring pixels can be used with zero cost:On the other hand, the following displacements result in costs greater than zero:Fig. 1 shows the first component, the straight connector component, which consists of a line of two different interchanging colors,here denoted by the two symbols◇and□. Given that the outside pixels are mapped to their respe ctive counterparts and the connector is continued infinitely, there are two possible ways in which the colored pixels can be mapped, namely to the left (i.e. f(2,j)=(2,j-1)) or to the right (i.e. f(2,j)=(2,j+1)),where the background pixels have different possibilities for the mapping, not influencing the main property of the connector. This property, which justifies the name ‘connector ’, is the following: It is not possible to find a mapping, which yields zero cost where the relative displacements of the connector pixels are not equal, i.e. one always has f(2,j)-(2,j)=f(2,j')-(2,j'),which can easily be observed by induction over j'.That is, given an initial displacement of one pixel (which will be ±1 in this context), the remaining end of the connector has the same displacement if overall costs of the mapping are zero. Given this property and the direction of a connector, which we define to be directed from variable to clause, wecan define the state of the connector as carrying the‘true’truth value, if the displacement is 1 pixel in the direction of the connector and as carrying the‘false’ truth value, if the displacement is -1 pixel in the direction of the connector. This property then ensures that the truth value transmitted by the connector cannot change at mappings of zero cost.Image A image Bmapping 1 mapping 2Fig. 1. The straight connector component with two possible zero cost mappings.For drawing of arbitrary graphs, clearly one also needs corners,which are represented in Fig. 2.By considering all possible displacements which guarantee overall cost zero, one can observe that the corner component also ensures the basic connector property. For example, consider the first depicted mapping, which has zero cost. On the other hand, the second mapping shows, that it is not possible to construct a zero cost mapping with both connectors‘leaving’the component. In that case, the pixel at the position marked‘? ’either has a conflict (that i s, introduces a cost greater than zero in the criterion function because of mapping mismatch) with the pixel above or to the right of it,if the same color is to be met and otherwise, a cost in the gray value mismatch term is introduced.image A image Bmapping 1 mapping 2Fig. 2. The corner connector component and two example mappings.Fig. 3 shows the variable component, in this case with two positive (to the left) and one negated output (to the right) leaving the component as connectors. Here, a fourth color is used, denoted by ·.This component has two possible mappings for thecolored pixels with zero cost, which map the vertical component of the source image to the left or the right vertical component in the target image, respectively. (In both cases the second vertical element in the target image is not a target of the mapping.) This ensures±1 pixel relative displacements at the entry to the connectors. This property again can be deducted by regarding all possible mappings of the two images.The property that follows (which is necessary for the use as variable) is that all zero cost mappings ensure that all positive connectors carry the same truth value,which is the opposite of the truth value for all the negated connectors. It is easy to see from this example how variable components for arbitrary numbers of positive and negated outputs can be constructed.image A image BImage C image DFig. 3. The variable component with two positive and one negated output and two possible mappings (for true and false truth value).Fig. 4 shows the most complex of the components, the clause component. This component consists of two parts. The first part is the horizontal connector with a 'bend' in it to the right.This part has the property that cost zero mappings are possible for all truth values of x and y with the exception of two 'false' values. This two input disjunction，can be extended to a three input dis-junction using the part in the lower left. If the z connector carries a 'false' truth value, this part can only be mapped one pixel downwards at zero cost.In that case the junction pixel (the fourth pixel in the third row) cannot be mapped upwards at zero cost and the 'two input clause' behaves as de-scribed above. On the other hand, if the z connector carries a 'true' truth value, this part can only be mapped one pixel upwards at zero cost,and the junction pixel can be mapped upwards,thus allowing both x and y to carry a 'false' truth value in a zero cost mapping. Thus there exists a zero cost mapping of the clause component iff at least one of the input connectors carries a truth value.image Aimage B mapping 1(true,true,false)mapping 2 (false,false,true,)Fig. 4. The clause component with three incoming connectors x, y , z and zero cost mappings forthe two cases(true,true,false)and (false, false, true).The described components are already sufficient to prove NP-completeness by reduction from planar 3-SAT (which is an NP-complete sub-problem of 3-SAT where the additional constraints on the instances is that the dependency graph is planar),but in order to derive a reduction from 3-SAT, we also include the possibility of crossing connectors.Fig. 5 shows the connector crossing, whose basic property is to allow zero cost mappings if the truth–values are consistently propagated. This is assured by a color change of the vertical connector and a 'flexible' middle part, which can be mapped to four different positions depending on the truth value distribution.image Aimage Bzero cost mappingFig. 5. The connector crossing component and one zero cost mapping.3.2. ReductionUsing the previously introduced components, we can now perform the reduction from 3-SAT to image matching .Proof of the claim that the image matching problem is NP-complete:Clearly, the image matching problem is in NP since, given a mapping f and two images A and B ,the computation of c（A,B,f）can be done in polynomial time. To prove NP-hardness, we construct a reduction from the 3-SAT problem. Given an instance of 3-SAT we construct two images A and B , for which a mapping of cost zero exists iff all the clauses can be satisfied.Given the dependency graph D ,we construct an embedding of the graph into a 2D pixel grid, placing the vertices on a large enough distance from each other (say100（K+L）² ).This can be done using well-known methods from graph drawing (see e.g.di Battista et al.,1999).From this image of the graph D we construct the two images A and B , using the components described above.Each vertex belonging to a variable is replaced with the respective parts of the variable component, having a number of leaving connectors equal to the number of incident edges under consideration of the positive or negative use in the respective clause. Each vertex belonging to a clause is replaced by the respective clause component,and each crossing of edges is replaced by the respective crossing component. Finally, all the edges are replaced with connectors and corner connectors, and the remaining pixels inside the rectangular hull of the construction are set to the background gray value. Clearly, the placement of the components can be done in such a way that all the components are at a large enough distance from each other, where the background pixels act as an 'insulation' against mapping of pixels, which do not belong to the same component. It can be easily seen, that the size of the constructed images is polynomial with respect to the number of vertices and edges of D and thus polynomial in the size of the instance of 3-SAT, at most in the order （K+L）².Furthermore, it can obviously be constructed in polynomial time, as the corresponding graph drawing algorithms are polynomial.Let there exist a truth assignment to the variables x1，…，xL, which satisfies allthe clauses c1，…，cK. We construct a mapping f , that satisfies c（f,A,B）=0 asfollows.For all pixels (i, j ) belonging to variable component l with A（i，j）not of the background color,set f（i，j）=（i，j-1）if xlis assigned the truth value 'true' , set f（i，j）=（i，j+1）, otherwise. For the remaining pixels of the variable component set A（i，j）=B（i，j），if f（i，j）=（i，j）, otherwise choose f（i，j）from{(i,j+1),(i+1,j+1),(i-1,j+1)}for xl'false' respectively from {(i,j-1),(i+1,j-1),(i-1,j-1)}for xl'true ',such that A（i，j）=B（f（i，j））. This assignment is always possible and has zero cost, as can be easily verified.For the pixels（i，j）belonging to (corner) connector components,the mapping function can only be extended in one way without the introduction of nonzero cost,starting from the connection with the variable component. This is ensured by thebasic connector property. By choosing f （i ，j ）=（i,j ）for all pixels of background color, we obtain a valid extension for the connectors. For the connector crossing components the extension is straight forward, although here ––as in the variable mapping ––some care must be taken with the assign ment of the background value pixels, but a zero cost assignment is always possible using the same scheme as presented for the variable mapping.It remains to be shown that the clause components can be mapped at zero cost, if at least one of the input connectors x , y , z carries a ' true' truth value.For a proof we regard alls even possibilities and construct a mapping for each case. In thedescription of the clause component it was already argued that this is possible,and due to space limitations we omit the formalization of the argument here.Finally, for all the pixels （i ，j ）not belonging to any of the components, we set f （i ，j ）=（i ，j ）thus arriving at a mapping function which has c （f ，A ，B ）=0。
A Threshold Selection Method from Gray-Level Histograms图像分割经典论文翻译(部分)
A Threshold Selection Method from Gray-Level Histograms[1][1]Otsu N, A threshold selection method from gray-level histogram. IEEE Transactions on System，Man，and Cybemetics，SMC-8，1978：62-66．一种由灰度直方图选取阈值的方法摘要介绍了一种对于画面分割自动阈值选择的非参数和无监督的方法。
最佳阈值由判别标准选择，即最大化通过灰度级所得到的类的方差。
这个过程很简单，是利用了灰度直方图0阶和第1阶的累积。
这是简单的方法扩展到多阈值的问题。
几种实验结果呈现也支持了方法的有效性。
一.简介选择灰度充分的阈值，从图片的背景中提取对象对于图像处理非常重要。
在这方面已经提出了多种技术。
在理想的情况下，直方图具有分别表示对象和背景的能力，两个峰之间有很深的明显的谷，使得阈值可以选择这个谷底。
然而，对于大多数实际图片，它常常难以精确地检测谷底，特别是在这种情况下，当谷是平的和广泛的，具有噪声充满时，或者当两个峰是在高度极其不等，通常不产生可追踪的谷。
已经出现了，为了克服这些困难，提出的一些技术。
它们是，例如，谷锐化技术[2]，这个技术限制了直方图与（拉普拉斯或梯度）的衍生物大于绝对值的像素，并且描述了绘制差分直方图方法[3]，选择灰度级的阈值与差的最大值。
这些利用在原始图象有关的信息的相邻像素（或边缘），修改直方图以便使其成为阈值是有用的。
另一类方法与参数方法的灰度直方图直接相关。
例如，该直方图在最小二乘意义上与高斯分布的总和近似，应用了统计决策程序 [4]。
然而，这种方法需要相当繁琐，有时不稳定的计算。
此外，在许多情况下，高斯分布与真实模型的近似值较小。
在任何情况下，没有一个阈值的评估标准能够对大多数的迄今所提出的方法进行评价。
这意味着，它可能是派生的最佳阈值方法来建立一个适当的标准，从更全面的角度评估阈值的“好与坏”的正确方法。
毕业设计外文文献翻译(原文+译文)
Environmental problems caused by Istanbul subway excavation and suggestionsfor remediation伊斯坦布尔地铁开挖引起的环境问题及补救建议Ibrahim Ocak Abstract：Many environmental problems caused by subway excavations have inevitably become an important point in city life. These problems can be categorized as transporting and stocking of excavated material, traffic jams, noise, vibrations, piles of dust mud and lack of supplies. Although these problems cause many difficulties,the most pressing for a big city like Istanbul is excava tion,since other listed difficulties result from it. Moreover, these problems are environmentally and regionally restricted to the period over which construction projects are underway and disappear when construction is finished. Currently, in Istanbul, there are nine subway construction projects in operation, covering approximately 73 km in length; over 200 km to be constructed in the near future. The amount of material excavated from ongoing construction projects covers approximately 12 million m3. In this study, problems—primarily, the problem with excavation waste(EW)—caused by subway excavation are analyzed and suggestions for remediation are offered.摘要：许多地铁开挖引起的环境问题不可避免地成为城市生活的重要部分。
毕设设计类外文翻译
Interior Design Supports Art Education: A Case StudyInterior design, as a field of study, is a rapidly growing area of interest – particularly for teenagers in the United States. Part of this interest stems from the proliferation ofdesign-related reality shows available through television media. Some art educators and curriculum specialists in the nation perceive the study of interior spaces as a ‘practical application’ of the arts.This article discusses an experiential design problem, originally used in higher education interior design studio courses that was modified and shared with students in third grade to address national academic standards. Later, this same project was modified for use with high school students in the educator’s community a nd with international design students in South Korea.Lastly, the project was presented in a workshop to art education students at a higher education institution. The project was modified to address (1) the age group level and (2) a topic relevant to the audience. Goals of the design project were: (1) to explore creative problem-solving, (2) to explore the application of design elements and principles, and (3) to increase student understanding of spatial relationships within an interior environment. Findings indicate that the project supported several visual art standards, including perception and community. This project may be of interest to current and future art educators and others interested in the potential of interior design content supporting art education.IntroductionThe design of interior spaces is a growing area of interest in the United States. Studies indicate that people spend 90 per cent of their time indoors, thereby making the quality design of interiors critical to the health and welfare of the population. Youth have been unconsciously encouraged since their childhood to develop awareness of their personal interior spaces and furnishings through popular storybooks they read that introduce the awareness of scale, proportion and ergonomics at a very young age (e.g. Three Little Bears and Alice in Wonderland). More recently, teens in the United States have become unexpectedly ‘hooked’ on design related reality shows such as Trading Spaces, Changing Rooms and Design on a Dime. Although Trading Spaces was originally intended for adults, according to the Wall Street Journal article titled ‘The Teen-Room Makeover’ (18 October 2002) the audience has more than 125,000 viewers aged 12 to 17 [1]. In support of that finding, a survey conducted in 2003 for a national chain of hardware stores discovered 65 per cent of teens said they have watched home improvement-related television shows [2].Teens seemingly have a growing interest in the design of interior spaces.In the United States in 2002, a qualitative study was developed to determine if interior design subject-matter could support national academic standards in elementary and secondary schools (kindergarten – twelfth grade) [3]. Findings of the study indicated that art educators and curriculum specialists perceived interior design to be supportive in meeting their standards as a type of ‘practical application’ of the arts. Perceptions of the curriculum specialists indicated they were looking for new ways to interpret fine art standards in their existing curriculum and that interior design offered one solution. As a result, the researcher, who was an interior design educator, was encouraged to identify and develop a project or lesson plan that could introduce children and youth to the importance of well-designed interior spaces yet support an art education standard in the nation.This article discusses an experiential interior design project that was modified from an exercise used in the freshman and sophomore college studio classes and shared with students in third grade, high school, and with international students in South Korea by this interior design educator. The educator was later invited to present this project to art education teachers at her university. The project supported several school district visual art standards, including perception and community. It was modified to address (1) the age group level and (2) a topic relevant to the audience. Goals of the design project were: (1) to explore creative problem solving, (2) to explore the application of design elements and principles, and (3) to increase student understanding of spatial relationships within an interior environment. This project may be of interest to current and future art educators and others interested in the potential of interior design content supporting visual art standards.Review of literatureThe review of literature briefly discusses (1) experiential learning theory, (2) findings from a qualitative study involving art educators, and (3) the interior design link with art education. The interior design project description and process of application will follow.Experiential learningExperiential learning theory, as an application of cognitive/perceptual models, is a tool toenhance the cognitive process of students. Specifically, the experiential learning cycleinvolves a concrete experience that leads to observations and reflections then to formation of abstract concepts and generalisations, before finally testing implications from concepts in new situations [4].The Association for Experiential Education defines experiential education astheprocess by which a learner constructs knowledge, skill and value from direct experience [5]. Drengson [6] defines experiential education as the process of practical engagement withconcepts and skills applied in a practical setting and delivered through physical and practical mental activity.One of the key components to enhance student learning is reflection. Dewey [7] suggests that to have meaning, an experience must be combined with thought. Kolb [8] suggests that reflections can offer a potential source of powerful data to link theory to practice. The mental engagement of an experiential learner can involve questioning, investigation, experimentation, curiosity, problem-solving, assuming responsibility, creativity and the construction of meaning [9].Experiential learning offers the spontaneous opportunity for learning, whether from unplanned moments, natural consequences, mistakes or successes [10]. Holistically, it involves not only the cognitive but also any combination of the senses, the emotions, and the physical [11].Qualitative study involving art educatorsIn 2001, a study was conducted to determine if interior design may be supportive tokindergarten – twelfth grade (K–12) teachers in meeting national academic standards,including the arts [12]. To understand perceptions of experts in interior design and elementary and secondary education, five focus group session sand six personal interviews were conducted with interior design educators, practitioners,K–12 teachers (elementary, junior high, and high school levels), national standards curriculum specialists (local and state level), and school-to-career curriculum specialists from June 2001 to April 2002[13].Focus group findings indicated that K–12teachers, at both elementary and secondary levels, felt that interior design could be supportive in meeting visual art standards because youth are frequently analysing their personal and public spaces. Participants described specific examples of interior design materials they currently needed in their course work to include: examples of good and bad interior spaces, information about elements and principles of design as they relate to interior spaces, and hands-on col our wheels of sturdy materials. In addition they requested that the materials be low cost, stimulating,‘touchable’,recyclable, self-contained, and fun. Lesson plans the visual art teachers suggested included:• reinvention of the ‘shoe box’ projec t;• development of well-known stories (The Three Pigs, Three Little Bears, and Alice in Wonderland) into space models to teach proportion and scale. In addition, it was suggestedthe following lesson plan: use of Goldilocks story to analyse ‘client or consumer needs’;• use of a Dr Seuss story (literary passage) to generate a conceptual model that enhances creativity;• study of cultural spaces at the junior high level that would enhance study of personal expression of identity in interiors [14].The visual arts curriculum specialists indicated hat interior design –as a ‘practical application’ should be introduced in elementary levels where there is a ‘small window of opportunity’ to give good information about the visual arts. See Table 1 fo r an example of the visual art standards in kindergarten – third grade levels. One visual art specialist advocated that the design process was more important to teach than a particular design method. He suggested moving students from designing personal spaces – and the study of elements and principles of design – in elementary levels to the analysis of private and public spaces in the junior high level. Then the high school levels could be reserved for additional indepth Exploration.Today, junior high and high school students are quite attracted to design-related reality shows. Over the last five years, the number of designrelated television shows has increased dramatically [15]. Why are these shows so attractive to teens and young adults? Rodriguez [16]has suggested that this interest is linked to the teens need for expression of self andself-identity.An individual’s unique identity is established through personalisation of space, which is critical to overall development of self [17]. Developing a sense of self involves the use of symbols to communicate to others one’s personal underlying identity.Interior design link with art educationIt is not common for interior design to be linked with art education in K–12 grade levels in the United States. However, the Foundation for Interior Design EducationResearch[18]standards and guidelines – the accreditation organization for higher education interior design programmes in the nation – reveal that there are many shared areas between visual arts and interior design (e.g.elements and principles of design).Rasmussen and Wright [19]advocate the need for a new model for art education. The new model should offer youth an aesthetic education that does more than just serve the traditional concerns of established arts curriculum. Experiences indicate that young people try to make sense of their own lives by creating contextual understanding through actively, and intentionally, making connections to signs, perceptions and experiences. This is a challenge to develop a new art education model that creates a balance between social andcontextual needs, knowledge of young people, and theaesthetic medium itself.The study of interior spaces offers one such context for learning in the physical environment.People spend 90 per cent of their time in interior spaces [20]. Youth consciously or unconsciously, analyse and respond to their near environment. They also learn best if they understand why they are learning what they are learning. Application of design and art to everyday life can assist in making connections in student learning, and develop more awareness of good design as well as an appreciation of the arts. Youth need theopportunity to learn more about design and human behavior so they can learn they have choices about how supportive their environments can be. Children can [determine] how design influences their behaviors; howdesign can be used to manipulate behavior; how design can encourage or discourage conversation, establish status, put people in power positions, increase or decrease anxiety [21].Therefore, based on (1) the experiential learning theoretical underpinnings, (2) recommendations made by art educators and curriculum specialists, and (3) a call for a new ways of teaching art education, an interior design educator at a higher education institution modified an experiential design project that involved the use of elements and principles of design and an opportunity for self-expression of personal spaces. The designproblem of the personal space was changed based on the grade level.Case study project descriptionAlthough art educators and curriculum specialists perceived that interior design content could be supportive to visual art standards, it was determined that a case study project needed to be developed and presented to various grade levels. It was also determined that a conceptual model of interior spaces should be used toenhance student creativity and exploration rather than a finite model that would offer too many rules and boundaries. Project descriptionThe experiential interior design project involved the construction of athree-dimensional concept model using 44 triangular and rectangular pieces of cardstock (stiff) paper in a neutral colour [22]. The objective was to discover, manipulate and create interior spaces based on a given design problem (e.g. design your space station on a planet of your choice or design your home in the Rocky Mountains of Colorado). The purpose ofthe project was to encourage students to design a conceptual structure from the interior out, keep-ing in mind the function of the building. The student’s model had to incorporate a minimum of six spaces and three levels to encourage vertical as well as horizontal volumes. All 44 pieces of cardstock had to be used in the finished model, which sometimes posed achallenge to the youth. The cardstock pieces could not be ripped, torn, or pierced. However, they could be bent and shapedaccording to the whim of the student.Flow from one space to another and one level to another was emphasized. The decision-making design process was explained and encouraged.Outcomes consisted of a three-dimensional abstract model which, if successfully executed, demonstrated the break-down of traditional spatial paradigms. Design problemsEach student grade level was given a different design problem based on the academic standards that were to be met in that class. In some cases, several academic standards were addressed at the same time. Two national standards for visual arts in the United States were selected to be supported with this project: communication and perception. The communication standard indicates that students in kindergarten – third grade should recognise the use of the visual arts as a means of communication (e.g. select and use visual images, themes and ideas in their own work). The perception standard indicates that students know, understand and apply elements of visual arts and principles of design (e.g. Identify elements and principles of design).Third grade studentsAfter procuring appropriate permission, the design educator brought volunteer college-age interior design students to the elementary school to help administer the project. Three third grade classes (twenty students in each class) had just finished a science unit on space and orbits and were studying specific visual art standards. The children were asked to design a personal space station on a planet of their choice. The goal was to help students relate the newly learned science information to something in real life (e.g. Their home), yet encourage exploration of visual arts (see Figs. 2–4).Each team of students was given the same 44 pieces of cardstock (all cut out) in a plastic bag, a cardboard base (15” x 15” square) on which to build the model, and cellophane tape to use in constructing the model. To enhance reflection of this experiential project, each team of three students was asked to give a two-minute verbal presentation in front of the class on their finished model. In this manner, they could discuss their design solution and the design educator could assess their use of creativity through design elements and principles.The college students and design educator rotated through the three classrooms of students to answer questions, encourage use of design elements and principles, and applaud their creative exploration. The third grade teachers assisted in supporting the structure of the class and encouraging shy students who were reluctant to begin.It was interesting to observe that the children rarely built the models on their provided classroom tables. Instead,they moved to the floor space, located the base for the model in between team members, and began construction. Each team member assumed a role in the process. One team member seemed t o act as the ‘designer’, one as the ‘builder/construction crew’ and the last as the ‘supplier’ of materials. Students excitedlydiscussed the positioning of the triangular pieces of cardstock in their model, their rooms in their space stations, and the different ways to turn the model to create different vantage points.The teams of third graders had one hour to complete the models. Then their verbal presentations began, interspersed with questions and comments from the design educator and third grade teachers. Informal observations indicatedthat application of design elements and principles was strong – perhaps due to the consistent rectangular and triangular shapes that had been provided – thereby supporting the visual arts perception standard. Manipulation of shapes was innovative. Line, shape and form were used to provide movement through adjoining spaces and offered a sense of verticality. Interior volumes were created that supported human behaveour in interior spaces. For example, one team’s presentation discussed how their space station boasted an exercise room with trampolines to strengthen human muscles that weakened as a result of zero gravity in outer space. The communication standard was supported in their finished models in a couple ways. First there was a theme of design as it relates to protection from foreign objects. For example, one team’s space station on Saturn incorporated a force field to protect it from flying rocks. Other visual themes of security and safety evoked the implementation of security cameras, alien detectors, missile launchers, telescope laboratories, control stations and transport rooms. Another visual theme related to circulation. Circulation within the structure was depicted by the third graders through the use of escalators, stairs, elevators and poles. A third visual theme was unique human needs as they relate to interior spaces. Almost every team’s space station incorporated a room for their mothers! In addition, depending on the students’ personal interests, unique space station features ranged from chemical rooms to sandboxes. It was obvious in their multiple unique design solutions their use of creativity had been explored and enhanced.Evaluation and assessment that took place, after the classes were dismissed, indicated that the third grade teachers perceived that this experiential design project supported the visual arts standards in both the communication and perception components as well as the third grade science academic standard concerning space and orbits. In addition, the experiential component of the project had unexpected results when certain quiet, unassuming students in the class became animated and highly engaged in learning. One teacher shared her excitement with the design educator about a new connection that wasformed with one of students that she had not been able to connect with before the design exercise.High school studentsAfter the case study with the third grade students, it was determined to offer this project to high school students. Diversity students in a nearby community were invited to attend a complimentary design workshop at a local library. The interior design educator was asked to present a design problem that would relate to arteducation (see Figs. 6–8).Their problem was to use the same experiential project and shapes to design and construct a conceptual model of their new home or cabin in the Rocky Mountain region. The same project constraints existed. Due to the students’ ages, discussions took place prior to the exercise about innovative problem-solving, the exploration of creativity and the elements and principles of design used within the design process. Some of these elements and principles included:Scale. Awareness of human scale was addressed to develop understanding of proportion and scale of the structure and interior spaces. Shape. Triangular shapes were deliberately selected to encourage students to break paradigms of rectangular interior spaces.Colour. The cardstock pieces were of a neutral colour to enhance spatial composition rather than draw attention to colour usage or juxtaposition. Volume/Mass. The mass of thethree-dimensional model was important in communicating the use of common elements and principles of design (e.g. line, rhythm). Line. A variety of different lines (e.g. diagonal, horizontal) were investigated in the manipulation of the shapes. Space. Space was created through the manipulation of shapes. Theories of complexity, mystery and refuge within interior spaces were discussed. Informal assessment of the finished design models indicated that the design solutions werevery creative.Later that semester, by invitation, the same design project was taken to college students training to be art educators in a mini-workshop format. The art education students found the exercise effective in enhancing creativity and understanding how interior design can enhance understanding of visual arts.International studentsAlthough there was no intention to meet a national visual arts academic standard at a specific grade level, this same experiential design project was presented in Seoul, South Korea to college-aged international students. The design problem was to use the same 44 pieces to develop a design concept model for acommercial building in Seoul. Language translators were used to help the design educatorintroduce the project, guide the students through the process, and understand their verbal presentations at the end of the workshop.Students commented during and after the workshop how the model enhanced their visual literacy skills (they used different words) and creativity within the context of everyday life. The experiential nature of the workshop was seemingly a pleasure to them (see Figs.9–11).Discussion and conclusionThis interior design case study project was designed to be experiential in nature to enhance student learning of the visual arts. Student and teacher assessment of the various groups indicated enthusiasm for the design project because it enhanced creativity, explored multiple design solutions, related to real life, and increased their understanding of human behaviour within the context of the physical environment. Teacherassessment of the age groups indicated that the project did support visual art standards at the appropriate grade level. In addition, their assessment indicated satisfaction with the manner in which the interior design project encouraged student usage of the design elements and principles and the application of design to everyday living. Several instructors indicated that quiet and shy students in their class became engaged in the learning process, which had not been previously observed. Perception of art educators and art education students was that this project supported a variety of visual art standards such as perception and communication. This interior design case study project can be modified for various age and cultural groups and may be of interest to educators who are interested in working collaboratively with colleagues from other disciplines.Visual art programmes in the United States are being cut from the K–12 curriculum. By linking visual arts to an up-and-coming aesthetic field, such as interior design, there may be new ways to sustain and grow visual art programmes in the nation.References1. Orndoff, K. (2003) ASID American Society of Interior Designers 2003 Strategic Environment Report. Future Impact Education, p. 9.2. Levitz, S. (2004) Teens Hooked on Home Décor, London Free Press (Ontario, CA), 24 June, p. D2.3. Clemons, S. (2002) Collaborative Links with K–12: A Proposed Model Integrating Interior Design with National Education Standards, Journal of Interior Design, Vol. 28, No. 1, pp.40–8.4. Rubin, S. G. (1983) Overcoming Obstacles to Institutionalization of Experiential Learning Programs, New Directions for Experiential Learning, Vol. 20, pp. 43–54.5. Luckman, C. (1996) Defining Experiential Education, Journal of Experiential Education, Vol. 19, No. 1, pp. 6–7.6. Drengson, A. R. (1995) What Means this Experience? in Kraft, R. J. & Sokofs, M. [Eds] The Theory of Experiential Education. Boulder, CO: Association for Experiential Education, pp. 87–93.7. Dewey, J. (1916) Democracy and Education. New York: Macmillan.8. Kolb, D. A. (1984). Experiential Learning: Experience as the Sources of Learning and Development. Englewood Cliffs, NJ: Prentice-Hall.9. Luckmann, C. op. cit.10. Ibid.11. Carver, R. (1996) Theory for Practice: A Framework for Thinking about Experiential Education, Journal of Experiential Education, Vol. 19, No. 1, pp. 8–13.12. Clemons, S. op. cit.13. Ibid.14. Ibid.15. Bien, L. (2003) Renovating how-to TV Shows in a Race to Duplicate Success of ‘Trading Spaces’. The Post Standard (Syracuse, NY), 31 October, p. E1.16. Rodriguez, E. M. (2003) Starting Young, Miami Herald, 28 December, p. H–1.17. Baillie S. & Goeters, P. (1997) Home as a Developmental Environment. Proceedings of the American Association of Housing Educators, New Orleans, LA, pp. 32–6.18. Foundation of Interior Design Education Research (FIDER) home page. Available from URL: / (Accessed 4th January 2005).19. Rasmussen, B & Wright, P. (2001) The theatre workshop as educational space: How imagined reality is voiced and conceived, International Journal of Education & the Arts, Vol. 2, No. 2, pp.1–13.20. Environmental Protection Agency (2006) An Introduction to Indoor Air Quality (online). Available from URL: /iaq/ ia-intro.html (Accessed 26th September 2006).21. InformeDesign (n.d.) Implications, Vol. 1, No. 2, p. 2 (online). Available from URL: /# (Accessed 4th January 2005).22. Curfman, J. & Clemons, S. (1992) From Forty-Four Pieces to a New Spatial Paradigm, in Birdsong, C. [Ed.] Proceedings of the Interior Design Educators Council Southwest Regional Meeting, New Orleans, pp. 2–4./detail/refdetail?tablename=SJWD_U&filename=SJWD00000744102&uid=WEEvR EcwSlJHSldSdnQ0SWZDdUlMV1dWZi9tOGkyYTBaTzBVQjVYeENXYVp4MVRJQjI3cmZRYS9YRmhvdnlxazJRPT 0=$9A4hF_YAuvQ5obgVAqNKPCYcEjKensW4IQMovwHtwkF4VYPoHbKxJw!!Interior Design in Augmented Reality EnvironmentABSTRACTThis article presents an application of Augmented Realitytechnology for interior design. Plus, an Educational InteriorDesign Project is reviewed. Along with the dramatic progress ofdigital technology, virtual information techniques are alsorequired for architectural projects. Thus, the new technology ofAugmented Reality offers many advantages for digitalarchitectural design and construction fields. AR is also beingconsidered as a new design approach for interior design. In an ARenvironment, the virtual furniture can be displayed and modifiedin real-time on the screen, allowing the user to have an interactiveexperience with the virtual furniture in a real-world environment.Here, AR environment is exploited as the new workingenvironment for architects in architectural design works, and thenthey can do their work conveniently as such collaborativediscussion through AR environment. Finally, this study proposesa newmethod for applying AR technology to interior designwork, where a user can view virtual furniture and communicatewith 3D virtual furniture data using a dynamic and flexible userinterface. Plus, all the properties of the virtual furniture can beadjusted using occlusion- based interaction method for a TangibleAugmented Reality. General TermsApplications of computer science in modeling, visualization andmultimedia, graphics and imaging, computer vision, human-computerinteraction, et al.KeywordsAugmented Reality, Tangible AR, CAAD, ARToolKit, Interiordesign.1. INTRODUCTIONVisualizing how a particular table or chair will look in a roombefore it is decorated is a difficult challenge for anyone. Hence,Augmented Reality (AR) technology has been proposed forinterior design applications by few previous authors, for example,Koller, C. Wooward, A. Petrovski; K. Hirokazu, et al. The relateddevices typically include data glassesconnected to a。
图像处理毕业设计
图像处理毕业设计图像处理是计算机科学中的重要研究方向之一，也是目前计算机视觉技术最为基础的理论和技术之一。
图像处理技术在多个领域具有广泛的应用，如医学影像处理、安防监控、数字娱乐、远程遥感等。
本篇文章将介绍一个基于图像处理的毕业设计课题，该课题思路创新、内容丰富，具有较高的实践和研究价值。
该毕业设计的主要内容是基于深度学习的图像识别与分类。
随着深度学习技术的发展，图像识别和分类已经在很多领域得到了广泛应用。
本课题将通过研究图像识别和分类的相关理论和算法，设计并实现一个高效、准确的图像分类系统。
首先，需要对深度学习中的卷积神经网络进行深入研究。
卷积神经网络（Convolutional Neural Network，简称CNN）是当前图像处理和计算机视觉领域最为主要的模型之一，具有较强的特征提取和图像分类能力。
通过学习CNN的结构和原理，可以掌握图像处理中的特征提取和图像分类算法。
其次，需要选择并标注合适的图像数据集。
数据集的选择决定了图像分类系统的性能和效果，因此需要选择具有代表性和多样性的数据集。
同时，对数据集进行标注，即为每个图像打上正确的标签，以便后续的训练和评估。
然后，需要设计和训练一个合适的卷积神经网络模型。
通过将图像数据输入到卷积神经网络中，并通过反向传播算法对网络参数进行优化，可以得到一个具有较好分类效果的模型。
最后，需要对训练好的卷积神经网络进行测试和评估。
通过将测试集中的图像输入到训练好的模型中，并对预测结果进行比对和评估，可以得到图像分类系统的准确率、召回率等性能指标。
通过图像处理的毕业设计，可以掌握深度学习算法在图像处理中的应用，进一步提高对计算机视觉领域的理解和实践能力。
同时，通过独立完成一个复杂的实际项目，也可以培养出较强的问题解决能力和团队协作能力。
综上所述，基于深度学习的图像识别与分类是一个具有挑战性和研究价值的毕业设计课题。
通过对卷积神经网络的研究、图像数据集的选择与标注、卷积神经网络模型的设计与训练以及测试与评估，可以完成一个高效、准确的图像分类系统。
毕业设计(论文)外文资料翻译(学生用)
毕业设计外文资料翻译学院：信息科学与工程学院专业：软件工程姓名： XXXXX学号： XXXXXXXXX外文出处： Think In Java (用外文写)附件： 1.外文资料翻译译文；2.外文原文。
附件1：外文资料翻译译文网络编程历史上的网络编程都倾向于困难、复杂，而且极易出错。
程序员必须掌握与网络有关的大量细节，有时甚至要对硬件有深刻的认识。
一般地，我们需要理解连网协议中不同的“层”（Layer）。
而且对于每个连网库，一般都包含了数量众多的函数，分别涉及信息块的连接、打包和拆包；这些块的来回运输；以及握手等等。
这是一项令人痛苦的工作。
但是，连网本身的概念并不是很难。
我们想获得位于其他地方某台机器上的信息，并把它们移到这儿；或者相反。
这与读写文件非常相似，只是文件存在于远程机器上，而且远程机器有权决定如何处理我们请求或者发送的数据。
Java最出色的一个地方就是它的“无痛苦连网”概念。
有关连网的基层细节已被尽可能地提取出去，并隐藏在JVM以及Java的本机安装系统里进行控制。
我们使用的编程模型是一个文件的模型；事实上，网络连接（一个“套接字”）已被封装到系统对象里，所以可象对其他数据流那样采用同样的方法调用。
除此以外，在我们处理另一个连网问题——同时控制多个网络连接——的时候，Java内建的多线程机制也是十分方便的。
本章将用一系列易懂的例子解释Java的连网支持。
15.1 机器的标识当然，为了分辨来自别处的一台机器，以及为了保证自己连接的是希望的那台机器，必须有一种机制能独一无二地标识出网络内的每台机器。
早期网络只解决了如何在本地网络环境中为机器提供唯一的名字。
但Java面向的是整个因特网，这要求用一种机制对来自世界各地的机器进行标识。
为达到这个目的，我们采用了IP（互联网地址）的概念。
IP以两种形式存在着：(1) 大家最熟悉的DNS（域名服务）形式。
我自己的域名是。
所以假定我在自己的域内有一台名为Opus的计算机，它的域名就可以是。
matlab图像处理外文翻译外文文献
matlab图像处理外文翻译外文文献附录A 英文原文Scene recognition for mine rescue robotlocalization based on visionCUI Yi-an(崔益安), CAI Zi-xing(蔡自兴), WANG Lu(王璐)Abstract：A new scene recognition system was presented based on fuzzy logic and hidden Markov model(HMM) that can be applied in mine rescue robot localization during emergencies. The system uses monocular camera to acquire omni-directional images of the mine environment where the robot locates. By adopting center-surround difference method, the salient local image regions are extracted from the images as natural landmarks. These landmarks are organized by using HMM to represent the scene where the robot is, and fuzzy logic strategy is used to match the scene and landmark. By this way, the localization problem, which is the scene recognition problem in the system, can be converted into the evaluation problem of HMM. The contributions of these skills make the system have the ability to deal with changes in scale, 2D rotation and viewpoint. The results of experiments also prove that the system has higher ratio of recognition and localization in both static and dynamic mine environments.Key words: robot location; scene recognition; salient image; matching strategy; fuzzy logic; hidden Markov model1 IntroductionSearch and rescue in disaster area in the domain of robot is a burgeoning and challenging subject[1]. Mine rescue robot was developed to enter mines during emergencies to locate possible escape routes for those trapped inside and determine whether it is safe for human to enter or not. Localization is a fundamental problem in this field. Localization methods based on camera can be mainly classified into geometric, topological or hybrid ones[2]. With its feasibility and effectiveness, scene recognition becomes one of the important technologies of topological localization.Currently most scene recognition methods are based on global image features and have two distinct stages: training offline and matching online.。
数字图像处理论文中英文对照资料外文翻译文献
第 1 页中英文对照资料外文翻译文献原 文To image edge examination algorithm researchAbstract ：Digital image processing took a relative quite young discipline,is following the computer technology rapid development, day by day obtains th widespread application.The edge took the image one kind of basic characteristic,in the pattern recognition, the image division, the image intensification as well as the image compression and so on in the domain has a more widesp application.Image edge detection method many and varied, in which based on brightness algorithm, is studies the time to be most long, the theory develo the maturest method, it mainly is through some difference operator, calculates its gradient based on image brightness the change, thus examines the edge, mainlyhas Robert, Laplacian, Sobel, Canny, operators and so on LOG 。
6、毕业设计(论文)外文翻译(原文)模板
编号：桂林电子科技大学信息科技学院毕业设计(论文)外文翻译（原文）系（部）：专业：学生姓名：学号：指导教师单位：姓名：职称：年月日1、所填写内容“居中”对齐，注意每项下划线长度一致，所填字体为三号字、宋体字。
2、A4纸打印；页边距要求如下：页边距上下各为2.5 厘米，左右边距各为2.5厘米。
正文：要求为小四号Times New Roman字体，行间距取固定值（设置值为20磅）；字符间距为默认值（缩放100%，间距：标准）。
页眉处“共X页”，X需要手动修改。
大功率LED散热的研究摘要：如何提高大功率LED的散热能力，是LED器件封装和器件应用设计要解决的核心问题。
介绍并分析了国内外大功率LED散热封装技术的研究现状，总结了其发展趋势与前景用途。
关键词：大功率LED；散热；封装1. 引言发光二极管（LED ）诞生至今，已经实现了全彩化和高亮度化，并在蓝光LED 和紫光LED 的基础上开发了白光LED ，它为人类照明史又带来了一次飞跃。
发光二极管（LED）具有低耗能、省电、寿命长、耐用等优点，因而被各方看好将取代传统照明成为未来照明光源。
而大功率LED 作为第四代电光源，赋有“绿色照明光源”之称，具有体积小、安全低电压、寿命长、电光转换效率高、响应速度快、节能、环保等优良特性，必将取代传统的白炽灯、卤钨灯和荧光灯而成为21世纪的新一代光源。
普通LED 功率一般为0.05W ，工作电流为20mA ，大功率LED可以达到1W，2W，甚至数十瓦！工作电流可以是几十毫安到几百毫安不等。
其特点具有体积小、耗电小、发热小、寿命长、响应速度快、安全低电压、耐候性好、方向性好等优点。
外罩可用PC管制作，耐高温达135 度，低温-45 度。
广泛应用在油田、石化、铁路、矿山、部队等特殊行业、舞台装饰、城市景观照明、显示屏以及体育场馆等，特种工作灯具中的具有广泛的应用前景。
但由于目前大功率白光LED 的转换效率还较低，光通量较小，成本较高等方面因素的制约，因此大功率白光LED 短期内的应用主要是一些特殊领域的特种工作灯具，中长期目标才能是通用照明领域。
数字图像处理英文原版及翻译
Digital Image Processing and Edge DetectionDigital Image ProcessingInterest in digital image processing methods stems from two principal application areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for autonomous machine perception.An image may be defined as a two-dimensional function, f(x, y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pixels, and pixels. Pixel is the term most widely used to denote the elements of a digital image.Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike humans, who are limited to the visual band of the electromagnetic (EM) spec- trum, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images generated by sources that humans are not accustomed to associating with images. These include ultra- sound, electron microscopy, and computer-generated images. Thus, digital image processing encompasses a wide and varied field of applications.There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vi- sion, start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images. We believe this to be a limiting and somewhat artificial boundary. For example, under this definition, even the trivial task of computing the average intensity of an image (which yields asingle number) would not be considered an image processing operation. On the other hand, there are fields such as computer vision whose ultimate goal is to use computers to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs. This area itself is a branch of artificial intelligence (AI) whose objective is to emulate human intelligence. The field of AI is in its earliest stages of infancy in terms of development, with progress having been much slower than originally anticipated. The area of image analysis (also called image understanding) is in be- tween image processing and computer vision.There are no clearcut boundaries in the continuum from image processing at one end to computer vision at the other. However, one useful paradigm is to consider three types of computerized processes in this continuum: low-, mid-, and high level processes. Low-level processes involve primitive opera- tions such as image preprocessing to reduce noise, contrast enhancement, and image sharpening. A low-level process is characterized by the fact that both its inputs and outputs are images. Mid-level processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects. A midlevel process is characterized by the fact that its inputs generally are images, but its outputs are attributes extracted from those images (e.g., edges, contours, and the identity of individual objects). Finally, higher level processing involves “making sense” of an ensemble of recognized objects, as in image analysis, and, at the far end of the continuum, performing the cognitive functions normally associated with vision.Based on the preceding comments, we see that a logical place of overlap between image processing and image analysis is the area of recognition of individual regions or objects in an image. Thus, what we call in this book digital image processing encompasses processes whose inputs and outputs are images and, in addition, encompasses processes that extract attributes from images, up to and including the recognition of individual objects. As a simple illustration to clarify these concepts, consider the area of automated analysis of text. The processes of acquiring an image of the area containing the text, preprocessing that image, extracting(segmenting) the individual characters, describing the characters in a form suitable for computer processing, and recognizing those individual characters are in the scope of what we call digital image processing in this book. Making sense of the content of the page may be viewed as being in the domain of image analysis and even computer vision, depending on the level of complexity implied by the statement “making sense.”As will become evident shortly, digital image processing, as we have defined it, is used successfully in a broad range of areas of exceptional social and economic value.The areas of application of digital image processing are so varied that some form of organization is desirable in attempting to capture the breadth of this field. One of the simplest ways to develop a basic understanding of the extent of image processing applications is to categorize images according to their source (e.g., visual, X-ray, and so on). The principal energy source for images in use today is the electromagnetic energy spectrum. Other important sources of energy include acoustic, ultrasonic, and electronic (in the form of electron beams used in electron microscopy). Synthetic images, used for modeling and visualization, are generated by computer. In this section we discuss briefly how images are generated in these various categories and the areas in which they are applied.Images based on radiation from the EM spectrum are the most familiar, especially images in the X-ray and visual bands of the spectrum. Electromagnet- ic waves can be conceptualized as propagating sinusoidal waves of varying wavelengths, or they can be thought of as a stream of massless particles, each traveling in a wavelike pattern and moving at the speed of light. Each massless particle contains a certain amount (or bundle) of energy. Each bundle of energy is called a photon. If spectral bands are grouped according to energy per photon, we obtain the spectrum shown in fig. below, ranging from gamma rays (highest energy) at one end to radio waves (lowest energy) at the other. The bands are shown shaded to convey the fact that bands of the EM spectrum are not distinct but rather transition smoothly from one to theother.Image acquisition is the first process. Note that acquisition could be as simple as being given an image that is already in digital form. Generally, the image acquisition stage involves preprocessing, such as scaling.Image enhancement is among the simplest and most appealing areas of digital image processing. Basically, the idea behind enhancement techniques is to bring out detail that is obscured, or simply to highlight certain features of interest in an image. A familiar example of enhancement is when we increase the contrast of an image because “it looks better.” It is important to keep in mind that enhancement is a very subjective area of image processing. Image restoration is an area that also deals with improving the appearance of an image. However, unlike enhancement, which is subjective, image restoration is objective, in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation. Enhancement, on the other hand, is based on human subjective preferences regarding what constitutes a “good”enhancement result.Color image processing is an area that has been gaining in importance because of the significant increase in the use of digital images over the Internet. It covers a number of fundamental concepts in color models and basic color processing in a digital domain. Color is used also in later chapters as the basis for extracting features of interest in an image.Wavelets are the foundation for representing images in various degrees of resolution. In particular, this material is used in this book for image data compression and for pyramidal representation, in which images are subdivided successively into smaller regions.Compression, as the name implies, deals with techniques for reducing the storage required to save an image, or the bandwidth required to transmit it.Although storage technology has improved significantly over the past decade, the same cannot be said for transmission capacity. This is true particularly in uses of the Internet, which are characterized by significant pictorial content. Image compression is familiar (perhaps inadvertently) to most users of computers in the form of image , such as the jpg used in the JPEG (Joint Photographic Experts Group) image compression standard.Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape. The material in this chapter begins a transition from processes that output images to processes that output image attributes.Segmentation procedures partition an image into its constituent parts or objects. In general, autonomous segmentation is one of the most difficult tasks in digital image processing. A rugged segmentation procedure brings the process a longway toward successful solution of imaging problems that require objects to be identified individually. On the other hand, weak or erratic segmentation algorithms almost always guarantee eventual failure. In general, the more accurate the segmentation, the more likely recognition is to succeed.Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the boundary of a region (i.e., the set of pixels separating one image region from another) or all the points in the region itself. In either case, converting the data to a form suitable for computer processing is necessary. The first decision that must be made is whether the data should be represented as a boundary or as a complete region. Boundary representation is appropriate when the focus is on external shape characteristics, such as corners and inflections. Regional representation is appropriate when the focus is on internal properties, such as texture or skeletal shape. In some applications, these representations complement each other. Choosing a representation is only part of the solution for trans- forming raw data into a form suitable for subsequent computer processing. A method must also be specified for describing the data so that features of interest are highlighted. Description, also called feature selection, deals with extracting attributes that result in some quantitative information of interest or are basic for differentiating one class of objects from another.Recognition is the process that assigns a label (e.g., “vehicle”) to an object based on its descriptors. As detailed before, we conclude our coverage of digital image processing with the development of methods for recognition of individual objects.So far we have said nothing about the need for prior knowledge or about the interaction between the knowledge base and the processing modules in Fig 2 above. Knowledge about a problem domain is coded into an image processing system in the form of a knowledge database. This knowledge may be as simple as detailing regions of an image where theinformation of interest is known to be located, thus limiting the search that has to be conducted in seeking that information. The knowledge base also can be quite complex, such as an interrelated list of all major possible defects in a materials inspection problem or an image database containing high-resolution satellite images of a region in connection with change-detection applications. In addition to guiding the operation of each processing module, the knowledge base also controls the interaction between modules. This distinction is made in Fig 2 above by the use of double-headed arrows between the processing modules and the knowledge base, as opposed to single-headed arrows linking the processing modules.Edge detectionEdge detection is a terminology in image processing and computer vision, particularly in the areas of feature detection and feature extraction, to refer to algorithms which aim at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities.Although point and line detection certainly are important in any discussion on segmentation,edge detection is by far the most common approach for detecting meaningful discounties in gray level.Although certain literature has considered the detection of ideal step edges, the edges obtained from natural images are usually not at all ideal step edges. Instead they are normally affected by one or several of the following effects:1.focal blur caused by a finite depth-of-field and finite point spread function; 2.penumbral blur caused by shadows created by light sources of non-zero radius; 3.shading at a smooth object edge; 4.local specularities or interreflections in the vicinity of object edges.A typical edge might for instance be the border between a block of red color and a block of yellow. In contrast a line (as can be extracted by a ridge detector) can be a small number of pixels of a different color on an otherwise unchanging background. For a line, there maytherefore usually be one edge on each side of the line.To illustrate why edge detection is not a trivial task, let us consider the problem of detecting edges in the following one-dimensional signal. Here, we may intuitively say that there should be an edge between the 4th and 5th pixels.If the intensity difference were smaller between the 4th and the 5th pixels and if the intensity differences between the adjacent neighbouring pixels were higher, it would not be as easy to say that there should be an edge in the corresponding region. Moreover, one could argue that this case is one in which there are several edges.Hence, to firmly state a specific threshold on how large the intensity change between two neighbouring pixels must be for us to say that there should be an edge between these pixels is not always a simple problem. Indeed, this is one of the reasons why edge detection may be a non-trivial problem unless the objects in the scene are particularly simple and the illumination conditions can be well controlled.There are many methods for edge detection, but most of them can be grouped into two categories,search-based and zero-crossing based. The search-based methods detect edges by first computing a measure of edge strength, usually a first-order derivative expression such as the gradient magnitude, and then searching for local directional maxima of the gradient magnitude using a computed estimate of the local orientation of the edge, usually the gradient direction. The zero-crossing based methods search for zero crossings in a second-order derivative expression computed from the image in order to find edges, usually the zero-crossings of the Laplacian of the zero-crossings of a non-linear differential expression, as will be described in the section on differential edge detection following below. As a pre-processing step to edge detection, a smoothing stage, typically Gaussian smoothing, is almost always applied (see also noise reduction).The edge detection methods that have been published mainly differ in the types of smoothing filters that are applied and the way the measures of edge strength are computed. As many edge detection methods rely on the computation of image gradients, they also differ in the types of filters used for computing gradient estimates in the x- and y-directions.Once we have computed a measure of edge strength (typically the gradient magnitude), the next stage is to apply a threshold, to decide whether edges are present or not at an image point. The lower the threshold, the more edges will be detected, and the result will be increasingly susceptible to noise, and also to picking out irrelevant features from the image. Conversely a high threshold may miss subtle edges, or result in fragmented edges.If the edge thresholding is applied to just the gradient magnitude image, the resulting edges will in general be thick and some type of edge thinning post-processing is necessary. For edges detected with non-maximum suppression however, the edge curves are thin by definition and the edge pixels can be linked into edge polygon by an edge linking (edge tracking) procedure. On a discrete grid, the non-maximum suppression stage can be implemented by estimating the gradient direction using first-order derivatives, then rounding off the gradient direction to multiples of 45 degrees, and finally comparing the values of the gradient magnitude in the estimated gradient direction.A commonly used approach to handle the problem of appropriate thresholds for thresholding is by using thresholding with hysteresis. This method uses multiple thresholds to find edges. We begin by using the upper threshold to find the start of an edge. Once we have a start point, we then trace the path of the edge through the image pixel by pixel, marking an edge whenever we are above the lower threshold. We stop marking our edge only when the value falls below our lower threshold. This approach makes the assumption that edges are likely to be in continuous curves, and allows us to follow a faint section of an edge we have previously seen, without meaning that every noisy pixel in the image is marked down as an edge. Still, however, we have the problem of choosing appropriate thresholdingparameters, and suitable thresholding values may vary over the image.Some edge-detection operators are instead based upon second-order derivatives of the intensity. This essentially captures the rate of change in the intensity gradient. Thus, in the ideal continuous case, detection of zero-crossings in the second derivative captures local maxima in the gradient.We can come to a conclusion that,to be classified as a meaningful edge point,the transition in gray level associated with that point has to be significantly stronger than the background at that point.Since we are dealing with local computations,the method of choice to determine whether a value is “significant” or not id to use a threshold.Thus we define a point in an image as being as being an edge point if its two-dimensional first-order derivative is greater than a specified criterion of connectedness is by definition an edge.The term edge segment generally is used if the edge is short in relation to the dimensions of the image.A key problem in segmentation is to assemble edge segments into longer edges.An alternate definition if we elect to use the second-derivative is simply to define the edge ponits in an image as the zero crossings of its second derivative.The definition of an edge in this case is the same as above.It is important to note that these definitions do not guarantee success in finding edge in an image.They simply give us a formalism to look for them.First-order derivatives in an image are computed using the gradient.Second-order derivatives are obtained using the Laplacian.数字图像处理和边缘检测数字图像处理在数字图象处理方法的兴趣从两个主要应用领域的茎：改善人类解释图像信息;和用于存储，传输，和表示用于自主机器感知图像数据的处理。
（完整版）图像处理本科毕业设计
（完整版）图像处理本科毕业设计摘要本文以VC++6.0做为编程语言，对图像降噪技术进行研究。
本文通过介绍位图的基本操作以及在图像中加入椒盐噪声的操作，从而进一步引出几种降噪方法。
本文分别介绍“均值滤波”、“中值滤波”以及“傅里叶降噪”和“小波降噪”四种算法，实现图像降噪。
详细介绍了其基本原理、实现方法以及具体算法，并对降噪效果加以比较与分析。
“均值滤波”把每个像素都用周围的8个像素来做均值操作，可以平滑图像，速度快，算法简单。
“中值滤波”是常用的非线性滤波方法,也是图像处理技术中最常用的预处理技术。
同时在“低通滤波”及“小波降噪”中分别引入“快速傅里叶变换”和“Mallat 算法”，使得其取得更快速的计算，有效地解决了其计算量太大，运算时间过长的弊端，从而达到更好的综合降噪效果。
关键词：图像降噪；滤波；傅里叶降噪；小波降噪AbstractTaking VC++6.0 as the programming language, this paper is a study about image noise reduction technology. Furthermore, introducing several noise reducing measures through the introduction of the basic processing and the operation to put the salt and pepper noise into the image.The paper introduces Averaging Filter, Median Filter,Fourier Lowpass Filtering and Wavelet Filter to achieve image noise reducing. Here we introduce the basic principles, implement methods, detailed arithmetic, and make comparison and analysis the noise reducing effects.Averaging Filter operates every pixel by using 8 pixels meanly. It can make the images smoothing, fast and easy to calculate. Median Filter Fourier is a common nonlinear filtering way andalso common preprocessing technique when processing images. Introducing FFT and Mallat Algorithm separately into Lowpass Filtering and Wavelet Filter, and then we can make faster calculating and solve the massive calculating more efficiently. Therefore, we can have a more effective noise reducing.Keywords：Image Noise Reduction；Filter；Fourier Filter；Wavelet filter毕业设计（论文）原创性声明和使用授权说明原创性声明本人郑重承诺：所呈交的毕业设计（论文），是我个人在指导教师的指导下进行的研究工作及取得的成果。
数字图像处理英文原版及翻译
数字图象处理英文原版及翻译Digital Image Processing: English Original Version and TranslationIntroduction:Digital Image Processing is a field of study that focuses on the analysis and manipulation of digital images using computer algorithms. It involves various techniques and methods to enhance, modify, and extract information from images. In this document, we will provide an overview of the English original version and translation of digital image processing materials.English Original Version:The English original version of digital image processing is a comprehensive textbook written by Richard E. Woods and Rafael C. Gonzalez. It covers the fundamental concepts and principles of image processing, including image formation, image enhancement, image restoration, image segmentation, and image compression. The book also explores advanced topics such as image recognition, image understanding, and computer vision.The English original version consists of 14 chapters, each focusing on different aspects of digital image processing. It starts with an introduction to the field, explaining the basic concepts and terminology. The subsequent chapters delve into topics such as image transforms, image enhancement in the spatial domain, image enhancement in the frequency domain, image restoration, color image processing, and image compression.The book provides a theoretical foundation for digital image processing and is accompanied by numerous examples and illustrations to aid understanding. It also includes MATLAB codes and exercises to reinforce the concepts discussed in each chapter. The English original version is widely regarded as a comprehensive and authoritative reference in the field of digital image processing.Translation:The translation of the digital image processing textbook into another language is an essential task to make the knowledge and concepts accessible to a wider audience. The translation process involves converting the English original version into the target language while maintaining the accuracy and clarity of the content.To ensure a high-quality translation, it is crucial to select a professional translator with expertise in both the source language (English) and the target language. The translator should have a solid understanding of the subject matter and possess excellent language skills to convey the concepts accurately.During the translation process, the translator carefully reads and comprehends the English original version. They then analyze the text and identify any cultural or linguistic nuances that need to be considered while translating. The translator may consult subject matter experts or reference materials to ensure the accuracy of technical terms and concepts.The translation process involves several stages, including translation, editing, and proofreading. After the initial translation, the editor reviews the translated text to ensure its coherence, accuracy, and adherence to the target language's grammar and style. The proofreader then performs a final check to eliminate any errors or inconsistencies.It is important to note that the translation may require adapting certain examples, illustrations, or exercises to suit the target language and culture. This adaptation ensures that the translated version resonates with the local audience and facilitates better understanding of the concepts.Conclusion:Digital Image Processing: English Original Version and Translation provides a comprehensive overview of the field of digital image processing. The English original version, authored by Richard E. Woods and Rafael C. Gonzalez, serves as a valuable reference for understanding the fundamental concepts and techniques in image processing.The translation process plays a crucial role in making this knowledge accessible to non-English speakers. It involves careful selection of a professional translator, thoroughunderstanding of the subject matter, and meticulous translation, editing, and proofreading stages. The translated version aims to accurately convey the concepts while adapting to the target language and culture.By providing both the English original version and its translation, individuals from different linguistic backgrounds can benefit from the knowledge and advancements in digital image processing, fostering international collaboration and innovation in this field.。
数字图像处理 外文翻译 外文文献 英文文献 数字图像处理
数字图像处理外文翻译外文文献英文文献数字图像处理Digital Image Processing1 IntroductionMany operators have been proposed for presenting a connected component n a digital image by a reduced amount of data or simplied shape. In general we have to state that the development, choice and modi_cation of such algorithms in practical applications are domain and task dependent, and there is no \best method". However, it isinteresting to note that there are several equivalences between published methods and notions, and characterizing such equivalences or di_erences should be useful to categorize the broad diversity of published methods for skeletonization. Discussing equivalences is a main intention of this report.1.1 Categories of MethodsOne class of shape reduction operators is based on distance transforms. A distance skeleton is a subset of points of a given component such that every point of this subset represents the center of a maximal disc (labeled with the radius of this disc) contained in the given component. As an example in this _rst class of operators, this report discusses one method for calculating a distance skeleton using the d4 distance function which is appropriate to digitized pictures. A second class of operators produces median or center lines of the digitalobject in a non-iterative way. Normally such operators locate critical points _rst, and calculate a speci_ed path through the object by connecting these points.The third class of operators is characterized by iterative thinning. Historically, Listing [10] used already in 1862 the term linear skeleton for the result of a continuous deformation of the frontier of a connected subset of a Euclidean space without changing the connectivity of the original set, until only a set of lines and points remains. Many algorithms in image analysis are based on this general concept of thinning. The goal is a calculation of characteristic properties of digital objects which are not related to size or quantity. Methods should be independent from the position of a set in the plane or space, grid resolution (for digitizing this set) or the shape complexity of the given set. In the literature the term \thinning" is not used - 1 -in a unique interpretation besides that it always denotes a connectivity preserving reduction operation applied to digital images, involving iterations of transformations of speci_ed contour points into background points. A subset Q _ I of object points is reduced by ade_ned set D in one iteration, and the result Q0 = Q n D becomes Q for the next iteration. Topology-preserving skeletonization is a special case of thinning resulting in a connected set of digital arcs or curves.A digital curve is a path p =p0; p1; p2; :::; pn = q such that pi is a neighbor of pi?1, 1 _ i _ n, and p = q. A digital curve is called simpleif each point pi has exactly two neighbors in this curve. A digital arc is a subset of a digital curve such that p 6= q. A point of a digital arc which has exactly one neighbor is called an end point of this arc. Within this third class of operators (thinning algorithms) we may classify with respect to algorithmic strategies: individual pixels are either removed in a sequential order or in parallel. For example, the often cited algorithm by Hilditch [5] is an iterative process of testing and deleting contour pixels sequentially in standard raster scan order. Another sequential algorithm by Pavlidis [12] uses the de_nition of multiple points and proceeds by contour following. Examples of parallel algorithms in this third class are reduction operators which transform contour points into background points. Di_erences between these parallel algorithms are typically de_ned by tests implemented to ensure connectedness in a local neighborhood. The notion of a simple point is of basic importance for thinning and it will be shown in this reportthat di_erent de_nitions of simple points are actually equivalent. Several publications characterize properties of a set D of points (to be turned from object points to background points) to ensure that connectivity of object and background remain unchanged. The report discusses some of these properties in order to justify parallel thinning algorithms.1.2 BasicsThe used notation follows [17]. A digital image I is a functionde_ned on a discrete set C , which is called the carrier of the image.The elements of C are grid points or grid cells, and the elements (p;I(p)) of an image are pixels (2D case) or voxels (3D case). The range of a (scalar) image is f0; :::Gmaxg with Gmax _ 1. The range of a binary image is f0; 1g. We only use binary images I in this report. Let hIi be the set of all pixel locations with value 1, i.e. hIi = I?1(1). The image carrier is de_ned on an orthogonal grid in 2D or 3D - 2 -space. There are two options: using the grid cell model a 2D pixel location p is a closed square (2-cell) in the Euclidean plane and a 3D pixel location is a closed cube (3-cell) in the Euclidean space, where edges are of length 1 and parallel to the coordinate axes, and centers have integer coordinates. As a second option, using the grid point model a 2D or 3D pixel location is a grid point.Two pixel locations p and q in the grid cell model are called 0-adjacent i_ p 6= q and they share at least one vertex (which is a 0-cell). Note that this speci_es 8-adjacency in 2D or 26-adjacency in 3D if the grid point model is used. Two pixel locations p and q in the grid cell model are called 1- adjacent i_ p 6= q and they share at least one edge (which is a 1-cell). Note that this speci_es 4-adjacency in 2D or 18-adjacency in 3D if the grid point model is used. Finally, two 3Dpixel locations p and q in the grid cell model are called 2-adjacent i_ p 6= q and they share at least one face (which is a 2-cell). Note that this speci_es 6-adjacency if the grid point model is used. Any of these adjacency relations A_, _ 2 f0; 1; 2; 4; 6; 18; 26g, is irreexive andsymmetric on an image carrier C. The _-neighborhood N_(p) of a pixel location p includes p and its _-adjacent pixel locations. Coordinates of 2D grid points are denoted by (i; j), with 1 _ i _ n and 1 _ j _ m; i; j are integers and n;m are the numbers of rows and columns of C. In 3Dwe use integer coordinates (i; j; k). Based on neighborhood relations wede_ne connectedness as usual: two points p; q 2 C are _-connected with respect to M _ C and neighborhood relation N_ i_ there is a sequence of points p = p0; p1; p2; :::; pn = q such that pi is an _-neighbor of pi?1, for 1 _ i _ n, and all points on this sequence are either in M or all in the complement of M. A subset M _ C of an image carrier is called _-connected i_ M is not empty and all points in M are pairwise _-connected with respect to set M. An _-component of a subset S of C is a maximal _-connected subset of S. The study of connectivity in digital images has been introduced in [15]. It follows that any set hIi consists of a number of _-components. In case of the grid cell model, a component is the union of closed squares (2D case) or closed cubes (3D case). The boundary of a 2-cell is the union of its four edges and the boundary of a 3-cell is the union of its six faces. For practical purposes it iseasy to use neighborhood operations (called local operations) on adigital image I which de_ne a value at p 2 C in the transformed image based on pixel- 3 -values in I at p 2 C and its immediate neighbors in N_(p).2 Non-iterative AlgorithmsNon-iterative algorithms deliver subsets of components in specied scan orders without testing connectivity preservation in a number of iterations. In this section we only use the grid point model.2.1 \Distance Skeleton" AlgorithmsBlum [3] suggested a skeleton representation by a set of symmetric points.In a closed subset of the Euclidean plane a point p is called symmetric i_ at least 2 points exist on the boundary with equal distances to p. For every symmetric point, the associated maximal discis the largest disc in this set. The set of symmetric points, each labeled with the radius of the associated maximal disc, constitutes the skeleton of the set. This idea of presenting a component of a digital image as a \distance skeleton" is based on the calculation of a speci_ed distance from each point in a connected subset M _ C to the complement of the subset. The local maxima of the subset represent a \distance skeleton". In [15] the d4-distance is specied as follows. De_nition 1 The distance d4(p; q) from point p to point q, p 6= q, is the smallest positive integer n such that there exists a sequence of distinct grid points p = p0,p1; p2; :::; pn = q with pi is a 4-neighbor of pi?1, 1 _ i _ n.If p = q the distance between them is de_ned to be zero. Thedistance d4(p; q) has all properties of a metric. Given a binary digital image. We transform this image into a new one which represents at each point p 2 hIi the d4-distance to pixels having value zero. The transformation includes two steps. We apply functions f1 to the image Iin standard scan order, producing I_(i; j) = f1(i; j; I(i; j)), and f2in reverse standard scan order, producing T(i; j) = f2(i; j; I_(i; j)), as follows:f1(i; j; I(i; j)) =8><>>:0 if I(i; j) = 0minfI_(i ? 1; j)+ 1; I_(i; j ? 1) + 1gif I(i; j) = 1 and i 6= 1 or j 6= 1- 4 -m+ n otherwisef2(i; j; I_(i; j)) = minfI_(i; j); T(i+ 1; j)+ 1; T(i; j + 1) + 1g The resulting image T is the distance transform image of I. Notethat T is a set f[(i; j); T(i; j)] : 1 _ i _ n ^ 1 _ j _ mg, and let T_ _ T such that [(i; j); T(i; j)] 2 T_ i_ none of the four points in A4((i; j)) has a value in T equal to T(i; j)+1. For all remaining points (i; j) let T_(i; j) = 0. This image T_ is called distance skeleton. Now weapply functions g1 to the distance skeleton T_ in standard scan order, producing T__(i; j) = g1(i; j; T_(i; j)), and g2 to the result of g1 in reverse standard scan order, producing T___(i; j) = g2(i; j; T__(i; j)), as follows:g1(i; j; T_(i; j)) = maxfT_(i; j); T__(i ? 1; j)? 1; T__(i; j ? 1) ? 1gg2(i; j; T__(i; j)) = maxfT__(i; j); T___(i + 1; j)? 1; T___(i; j + 1) ? 1gThe result T___ is equal to the distance transform image T. Both functions g1 and g2 de_ne an operator G, with G(T_) = g2(g1(T_)) = T___, and we have [15]: Theorem 1 G(T_) = T, and if T0 is any subset of image T (extended to an image by having value 0 in all remaining positions) such that G(T0) = T, then T0(i; j) = T_(i; j) at all positions of T_with non-zero values. Informally, the theorem says that the distance transform image is reconstructible from the distance skeleton, and it is the smallest data set needed for such a reconstruction. The useddistance d4 di_ers from the Euclidean metric. For instance, this d4-distance skeleton is not invariant under rotation. For an approximation of the Euclidean distance, some authors suggested the use of di_erent weights for grid point neighborhoods [4]. Montanari [11] introduced a quasi-Euclidean distance. In general, the d4-distance skeleton is a subset of pixels (p; T(p)) of the transformed image, and it is not necessarily connected.2.2 \Critical Points" AlgorithmsThe simplest category of these algorithms determines the midpointsof subsets of connected components in standard scan order for each row. Let l be an index for the number of connected components in one row of the original image. We de_ne the following functions for 1 _ i _ n: ei(l) = _ j if this is the lth case I(i; j) = 1 ^ I(i; j ? 1) = 0 in row i, counting from the left, with I(i;?1) = 0 ，oi(l) = _ j if this is the lth case I(i; j) = 1- 5 -^ I(i; j+ 1) = 0 ，in row i, counting from the left, with I(i;m+ 1)= 0 ，mi(l) = int((oi(l) ?ei(l)=2)+ oi(l) ，The result of scanning row i is a set ofcoordinates (i;mi(l)) ofof the connected components in row i. The set of midpoints of all rows midpoints ，constitutes a critical point skeleton of an image I. This method is computationally eÆcient.The results are subsets of pixels of the original objects, and these subsets are not necessarily connected. They can form \noisy branches" when object components are nearly parallel to image rows. They may be useful for special applications where the scanning direction is approximately perpendicular to main orientations of object components.References[1] C. Arcelli, L. Cordella, S. Levialdi: Parallel thinning ofbinary pictures. Electron. Lett. 11:148{149, 1975}.[2] C. Arcelli, G. Sanniti di Baja: Skeletons of planar patterns. in: Topolog- ical Algorithms for Digital Image Processing (T. Y. Kong, A. Rosenfeld, eds.), North-Holland, 99{143, 1996.}[3] H. Blum: A transformation for extracting new descriptors of shape. in: Models for the Perception of Speech and Visual Form (W. Wathen- Dunn, ed.), MIT Press, Cambridge, Mass., 362{380, 1967.19} - 6 -数字图像处理1引言许多研究者已提议提出了在数字图像里的连接组件是由一个减少的数据量或简化的形状。
介绍数字图像处理外文翻译
附录1 外文原文Source: "the 21st century literature the applied undergraduate electronic communication series of practical teaching planThe information and communication engineering specialty in English ch02_1. PDF 120-124Ed: HanDing ZhaoJuMin, etcText A: An Introduction to Digital Image Processing1. IntroductionDigital image processing remains a challenging domain of programming for several reasons. First the issue of digital image processing appeared relatively late in computer history. It had to wait for the arrival of the first graphical operating systems to become a true matter. Secondly, digital image processing requires the most careful optimizations especially for real time applications. Comparing image processing and audio processing is a good way to fix ideas. Let us consider the necessary memory bandwidth for examining the pixels of a 320x240, 32 bits bitmap, 30 times a second: 10 Mo/sec. Now with the same quality standard, an audio stereo wave real time processing needs 44100 (samples per second) x 2 (bytes per sample per channel) x 2(channels) = 176Ko/sec, which is 50 times less.Obviously we will not be able to use the same techniques for both audio and image signal processing. Finally, digital image processing is by definition a two dimensions domain; this somehow complicates things when elaborating digital filters.We will explore some of the existing methods used to deal with digital images starting by a very basic approach of color interpretation. As a moreadvanced level of interpretation comes the matrix convolution and digital filters. Finally, we will have an overview of some applications of image processing.The aim of this document is to give the reader a little overview of the existing techniques in digital image processing. We will neither penetrate deep into theory, nor will we in the coding itself; we will more concentrate on the algorithms themselves, the methods. Anyway, this document should be used as a source of ideas only, and not as a source of code. 2. A simple approach to image processing(1) The color data: Vector representation①BitmapsThe original and basic way of representing a digital colored image in a computer's memory is obviously a bitmap. A bitmap is constituted of rows of pixels, contraction of the word s “Picture Element”. Each pixel has a particular value which determines its appearing color. This value is qualified by three numbers giving the decomposition of the color in the three primary colors Red, Green and Blue. Any color visible to human eye can be represented this way. The decomposition of a color in the three primary colors is quantified by a number between 0 and 255. For example, white will be coded as R = 255, G = 255, B = 255; black will be known as (R,G,B)= (0,0,0); and say, bright pink will be : (255,0,255). In other words, an image is an enormous two-dimensional array of color values, pixels, each of them coded on 3 bytes, representing the three primary colors. This allows the image to contain a total of 256×256×256 = 16.8 million different colors. This technique is also known as RGB encoding, and is specifically adapted to human vision. With cameras or other measure instruments we are capable of “seeing”thousands of other “colors”, in which cases the RG B encoding is inappropriate.The range of 0-255 was agreed for two good reasons: The first is that the human eye is not sensible enough to make the difference between more than 256 levels of intensity (1/256 = 0.39%) for a color. That is to say, an image presented to a human observer will not be improved by using more than 256 levels of gray (256shades of gray between black and white). Therefore 256 seems enough quality. The second reason for the value of 255 is obviously that it is convenient for computer storage. Indeed on a byte, which is the computer's memory unit, can be coded up to 256 values.As opposed to the audio signal which is coded in the time domain, the image signal is coded in a two dimensional spatial domain. The raw image data is much more straightforward and easy to analyze than the temporal domain data of the audio signal. This is why we will be able to do lots of stuff and filters for images without transforming the source data, while this would have been totally impossible for audio signal. This first part deals with the simple effects and filters you can compute without transforming the source data, just by analyzing the raw image signal as it is.The standard dimensions, also called resolution, for a bitmap are about 500 rows by 500 columns. This is the resolution encountered in standard analogical television and standard computer applications. You can easily calculate the memory space a bitmap of this size will require. We have 500×500 pixels, each coded on three bytes, this makes 750 Ko. It might not seem enormous compared to the size of hard drives, but if you must deal with an image in real time then processing things get tougher. Indeed rendering images fluidly demands a minimum of 30 images per second, the required bandwidth of 10 Mo/sec is enormous. We will see later that the limitation of data access and transfer in RAM has a crucial importance in image processing, and sometimes it happens to be much more important than limitation of CPU computing, which may seem quite different from what one can be used to in optimization issues. Notice that, with modern compression techniques such as JPEG 2000, the total size of the image can be easily reduced by 50 times without losing a lot of quality, but this is another topic.②Vector representation of colorsAs we have seen, in a bitmap, colors are coded on three bytes representing their decomposition on the three primary colors. It sounds obvious to a mathematician to immediately interpret colors as vectors in athree-dimension space where each axis stands for one of the primary colors. Therefore we will benefit of most of the geometric mathematical concepts to deal with our colors, such as norms, scalar product, projection, rotation or distance. This will be really interesting for some kind of filters we will see soon. Figure 1 illustrates this new interpretation:Figure 1(2) Immediate application to filters① Edge DetectionFrom what we have said before we can quantify the 'difference' between two colors by computing the geometric distance between the vectors representing those two colors. Lets consider two colors C1 = (R1,G1,B1) and C2 = (R2,B2,G2), the distance between the two colors is given by the formula :D(C1, C2) =(R1+This leads us to our first filter: edge detection. The aim of edge detection is to determine the edge of shapes in a picture and to be able to draw a resultbitmap where edges are in white on black background (for example). The idea is very simple; we go through the image pixel by pixel and compare the color of each pixel to its right neighbor, and to its bottom neighbor. If one of these comparison results in a too big difference the pixel studied is part of an edge and should be turned to white, otherwise it is kept in black. The fact that we compare each pixel with its bottom and right neighbor comes from the fact that images are in two dimensions. Indeed if you imagine an image with only alternative horizontal stripes of red and blue, the algorithms wouldn't see the edges of those stripes if it only compared a pixel to its right neighbor. Thus the two comparisons for each pixel are necessary.This algorithm was tested on several source images of different types and it gives fairly good results. It is mainly limited in speed because of frequent memory access. The two square roots can be removed easily by squaring the comparison; however, the color extractions cannot be improved very easily. If we consider that the longest operations are the get pixel function and put pixel functions, we obtain a polynomial complexity of 4*N*M, where N is the number of rows and M the number of columns. This is not reasonably fast enough to be computed in realtime. For a 300×300×32 image I get about 26 transforms per second on an Athlon XP 1600+. Quite slow indeed.Here are the results of the algorithm on an example image:A few words about the results of this algorithm: Notice that the quality of the results depends on the sharpness of the source image. Ifthe source image is very sharp edged, the result will reach perfection. However if you have a very blurry source you might want to make it pass through a sharpness filter first, which we will study later. Another remark, you can also compare each pixel with its second or third nearest neighbors on the right and on the bottom instead of the nearest neighbors. The edges will be thicker but also more exact depending on the source image's sharpness. Finally we will see later on that there is another way to make edge detection with matrix convolution.②Color extractionThe other immediate application of pixel comparison is color extraction.Instead of comparing each pixel with its neighbors, we are going to compare it with a given color C1. This algorithm will try to detect all the objects in the image that are colored with C1. This was quite useful for robotics for example. It enables you to search on streaming images for a particular color. You can then make you robot go get a red ball for example. We will call the reference color, the one we are looking for in the image C0 = (R0,G0,B0).Once again, even if the square root can be easily removed it doesn't really improve the speed of the algorithm. What really slows down the whole loop is the NxM get pixel accesses to memory and put pixel. This determines the complexity of this algorithm: 2xNxM, where N and M are respectively the numbers of rows and columns in the bitmap. The effective speed measured on my computer is about 40 transforms per second on a 300x300x32 source bitmap.3.JPEG image compression theory(一)JPEG compression is divided into four steps to achieve:(1) Color mode conversion and samplingRGB color system is the most common ways that color. JPEG uses a YCbCr colorsystem. Want to use JPEG compression method dealing with the basic full-color images, RGB color mode to first image data is converted to YCbCr color model data. Y representative of brightness, Cb and Cr represents the hue, saturation. By the following calculation to be completed by data conversion. Y = 0.2990R +0.5870 G+0.1140 B Cb =- 0.1687R-0.3313G +0.5000 B +128 Cr = 0.5000R-0.4187G-0.0813B+128 of human eyes on the low-frequency data than high-frequency data with higher The sensitivity, in fact, the human eye to changes in brightness than to color changes should be much more sensitive, ie Y component of the data is more important. Since the Cb and Cr components is relatively unimportant component of the data comparison, you can just take part of the data to deal with. To increase the compression ratio. JPEG usually have two kinds of sampling methods: YUV411 and YUV422, they represent is the meaning of Y, Cb and Cr data sampling ratio of three components.（2）DCT transformationThe full name is the DCT-discrete cosine transform (Discrete Cosine Transform), refers to a group of light intensity data into frequency data, in order that intensity changes of circumstances. If the modification of high-frequency data do, and then back to the original form of data, it is clear there are some differences with the original data, but the human eye is not easy to recognize. Compression, the original image data is divided into 8 * 8 matrix of data units. JPEG entire luminance and chrominance Cb matrix matrix, saturation Cr matrix as a basic unit called the MCU. Each MCU contains a matrix of no more than 10. For example, the ratio of rows and columns Jie Wei 4:2:2 sampling, each MCU will contain four luminance matrix, a matrix and a color saturation matrix. When the image data is divided into an 8 * 8 matrix, you must also be subtracted for each value of 128, and then a generation of formula into the DCT transform can be achieved by DCT transform purposes. The image data value must be reduced by 128, because the formula accepted by the DCT-figure range is between -128 to +127.（3）QuantizationImage data is converted to the frequency factor, you still need to accept a quantitative procedure to enter the coding phase. Quantitative phase requires two 8 * 8 matrix of data, one is to deal specifically with the brightness of the frequency factor, the other is the frequency factor for the color will be the frequency coefficient divided by the value of quantization matrix to obtain the nearest whole number with the quotient, that is completed to quantify. When the frequency coefficients after quantization, will be transformed into the frequency coefficients from the floating-point integer This facilitate the implementation of the final encoding. However, after quantitative phase, all the data to retain only the integer approximation, also once again lost some data content.（4）CodingHuffman encoding without patent issues, to become the most commonly used JPEG encoding, Huffman coding is usually carried out in a complete MCU. Coding, each of the DC value matrix data 63 AC value, will use a different Huffman code tables, while the brightness and chroma also require a different Huffman code tables, it needs a total of four code tables, in order to successfully complete the JPEG coding. DC Code DC is a color difference pulse code modulation using the difference coding method, which is in the same component to obtain an image of each DC value and the difference between the previous DC value to encode. DC pulse code using the main reason for the difference is due to a continuous tone image, the difference mostly smaller than the original value of the number of bits needed to encode the difference will be more than the original value of the number of bits needed to encode the less. For example, a margin of 5, and its binary representation of a value of 101, if the difference is -5, then the first changed to a positive integer 5, and then converted into its 1's complement binary number can be. The so-called one's complement number, that is, if the value is 0 for each Bit, then changed to 1; Bit is 1, it becomes 0. Difference between the five should retain the median 3, the following table that lists the difference between the Bit to be retained and the difference between the number of content controls.In the margin of the margin front-end add some additional value Hoffman code, such as the brightness difference of 5 (101) of the median of three, then the Huffman code value should be 100, the two connected together shall be 100101. The following two tables are the brightness and chroma DC difference encoding table. According to these two forms content, you can add the difference for the DC value Huffman code to complete the DC coding.4. ConclusionsDigital image processing is far from being a simple transpose of audiosignal principles to a two dimensions space. Image signal has its particular properties, and therefore we have to deal with it in a specificway. The Fast Fourier Transform, for example, which was such a practical tool in audio processing, becomes useless in image processing. Oppositely, digital filters are easier to create directly, without any signal transforms, in image processing.Digital image processing has become a vast domain of modern signal technologies. Its applications pass far beyond simple aesthetical considerations, and they include medical imagery, television and multimedia signals, security, portable digital devices, video compression,and even digital movies. We have been flying over some elementarynotions in image processing but there is yet a lot more to explore. Ifyou are beginning in this topic, I hope this paper will have given you thetaste and the motivation to carry on.附录2 外文翻译文献出处：《21 世纪全国应用型本科电子通信系列实用规划教材》之《信息与通信工程专业英语》ch02_1.pdf 120-124页主编：韩定定、赵菊敏等正文：介绍数字图像处理1.导言有几个原因使数字图像处理仍然是一个具有挑战性的领域。
论文英文翻译最终版
序号（学号）：040940131长春光华学院毕业设计（论文）译文Electronic technique电子技术姓名盛遵义教学院电气信息学院专业电子信息工程班级电信09401指导教师张淑艳（讲师）2013 年04 月10 日┊┊┊┊┊┊┊┊┊┊┊┊┊装┊┊┊┊┊订┊┊┊┊┊线┊┊┊┊┊┊┊┊┊┊┊┊┊Electronic techniqueFrom the world of radio in the world to a single chip， modern computer technology， industrial revolution， the world economy from the capital into the economy to knowledge economy。
Field in the electronic world， from the 20th century into the era of radio to computer technology in the 21st century as the center of the intelligent modern era of electronic systems。
The basic core of modern electronic systems are embedded computer systems (referred to as embedded systems)， while the microcontroller is the most typical and most extensive and most popular embedded systems。
radio has created generations of excellence in the world Fifties and sixties in the 20th century，the most representative of the advanced electronic technology is wireless technology， including radio broadcasting， radio，wireless communications (telegraph)，Amateur Radio， radio positioning，navigation and other telemetry， remote control， remote technology。
毕业设计外文文献翻译
毕业设计外文文献翻译Graduation Design Foreign Literature Translation (700 words) Title: The Impact of Artificial Intelligence on the Job Market Introduction:Artificial Intelligence (AI) is a rapidly growing field that has the potential to revolutionize various industries and job markets. With advancements in technologies such as machine learning and natural language processing, AI has become capable of performing tasks traditionally done by humans. This has raised concerns about the future of jobs and the impact AI will have on the job market. This literature review aims to explore the implications of AI on employment and job opportunities.AI in the Workplace:AI technologies are increasingly being integrated into the workplace, with the aim of automating routine and repetitive tasks. For example, automated chatbots are being used to handle customer service queries, while machine learning algorithms are being employed to analyze large data sets. This has resulted in increased efficiency and productivity in many industries. However, it has also led to concerns about job displacement and unemployment.Job Displacement:The rise of AI has raised concerns about job displacement, as AI technologies are becoming increasingly capable of performing tasks previously done by humans. For example, automated machines can now perform complex surgeries with greaterprecision than human surgeons. This has led to fears that certain jobs will become obsolete, leading to unemployment for those who were previously employed in these industries.New Job Opportunities:While AI might potentially replace certain jobs, it also creates new job opportunities. As AI technologies continue to evolve, there will be a greater demand for individuals with technical skills in AI development and programming. Additionally, jobs that require human interaction and emotional intelligence, such as social work or counseling, may become even more in demand, as they cannot be easily automated.Job Transformation:Another potential impact of AI on the job market is job transformation. AI technologies can augment human abilities rather than replacing them entirely. For example, AI-powered tools can assist professionals in making decisions, augmenting their expertise and productivity. This may result in changes in job roles and the need for individuals to adapt their skills to work alongside AI technologies.Conclusion:The impact of AI on the job market is still being studied and debated. While AI has the potential to automate certain tasks and potentially lead to job displacement, it also presents opportunities for new jobs and job transformation. It is essential for individuals and organizations to adapt and acquire the necessary skills to navigate these changes in order to stay competitive in the evolvingjob market. Further research is needed to fully understand the implications of AI on employment and job opportunities.。
毕业设计英文 翻译(原文)
编号：毕业设计(论文)外文翻译（原文）院（系）：桂林电子科技大学专业：电子信息工程学生姓名： xx学号： xxxxxxxxxxxxx 指导教师单位：桂林电子科技大学姓名： xxxx职称： xx2014年x月xx日Timing on and off power supplyusesThe switching power supply products are widely used in industrial automation and control, military equipment, scientific equipment, LED lighting, industrial equipment,communications equipment,electrical equipment,instrumentation, medical equipment, semiconductor cooling and heating, air purifiers, electronic refrigerator, LCD monitor, LED lighting, communications equipment, audio-visual products, security, computer chassis, digital products and equipment and other fields.IntroductionWith the rapid development of power electronics technology, power electronics equipment and people's work, the relationship of life become increasingly close, and electronic equipment without reliable power, into the 1980s, computer power and the full realization of the switching power supply, the first to complete the computer Power new generation to enter the switching power supply in the 1990s have entered into a variety of electronic, electrical devices, program-controlled switchboards, communications, electronic testing equipment power control equipment, power supply, etc. have been widely used in switching power supply, but also to promote the rapid development of the switching power supply technology .Switching power supply is the use of modern power electronics technology to control the ratio of the switching transistor to turn on and off to maintain a stable output voltage power supply, switching power supply is generally controlled by pulse width modulation (PWM) ICs and switching devices (MOSFET, BJT) composition. Switching power supply and linear power compared to both the cost and growth with the increase of output power, but the two different growth rates. A power point, linear power supply costs, but higher than the switching power supply. With the development of power electronics technology and innovation, making the switching power supply technology to continue to innovate, the turning points of this cost is increasingly move to the low output power side, the switching power supply provides a broad space for development.The direction of its development is the high-frequency switching power supply, high frequency switching power supply miniaturization, and switching power supply into a wider range of application areas, especially in high-tech fields, and promote the miniaturization of high-tech products, light of. In addition, the development and application of the switching power supply in terms of energy conservation, resource conservation and environmental protection are of great significance.classificationModern switching power supply, there are two: one is the DC switching power supply; the other is the AC switching power supply. Introduces only DC switching power supply and its function is poor power quality of the original eco-power (coarse) - such as mains power or battery power, converted to meet the equipment requirements of high-quality DC voltage (Varitronix) . The core of the DC switching power supply DC / DC converter. DC switching power supply classification is dependent on the classification of DC / DC converter. In other words, the classification of the classification of the DC switching power supply and DC/DC converter is the classification of essentially the same, the DC / DC converter is basically a classification of the DC switching power supply.DC /DC converter between the input and output electrical isolation can be divided into two categories: one is isolated called isolated DC/DC converter; the other is not isolated as non-isolated DC / DC converter.Isolated DC / DC converter can also be classified by the number of active power devices. The single tube of DC / DC converter Forward (Forward), Feedback (Feedback) two. The double-barreled double-barreled DC/ DC converter Forward (Double Transistor Forward Converter), twin-tube feedback (Double Transistor Feedback Converter), Push-Pull (Push the Pull Converter) and half-bridge (Half-Bridge Converter) four. Four DC / DC converter is the full-bridge DC / DC converter (Full-Bridge Converter).Non-isolated DC / DC converter, according to the number of active power devices can be divided into single-tube, double pipe, and four three categories. Single tube to a total of six of the DC / DC converter, step-down (Buck) DC / DC converter, step-up (Boost) DC / DC converters, DC / DC converter, boost buck (Buck Boost) device of Cuk the DC / DC converter, the Zeta DC / DC converter and SEPIC, the DC / DC converter. DC / DC converters, the Buck and Boost type DC / DC converter is the basic buck-boost of Cuk, Zeta, SEPIC, type DC / DC converter is derived from a single tube in this six. The twin-tube cascaded double-barreled boost (buck-boost) DC / DC converter DC / DC converter. Four DC / DC converter is used, the full-bridge DC / DC converter (Full-Bridge Converter).Isolated DC / DC converter input and output electrical isolation is usually transformer to achieve the function of the transformer has a transformer, so conducive to the expansion of the converter output range of applications, but also easy to achieve different voltage output , or a variety of the same voltage output.Power switch voltage and current rating, the converter's output power is usually proportional to the number of switch. The more the number of switch, the greater the output power of the DC / DC converter, four type than the two output power is twice as large,single-tube output power of only four 1/4.A combination of non-isolated converters and isolated converters can be a single converter does not have their own characteristics. Energy transmission points, one-way transmission and two-way transmission of two DC / DC converter. DC / DC converter with bi-directional transmission function, either side of the transmission power from the power of lateral load power from the load-lateral side of the transmission power.DC / DC converter can be divided into self-excited and separately controlled. With the positive feedback signal converter to switch to self-sustaining periodic switching converter, called self-excited converter, such as the the Luo Yeer (Royer,) converter is a typical push-pull self-oscillating converter. Controlled DC / DC converter switching device control signal is generated by specialized external control circuit.the switching power supply.People in the field of switching power supply technology side of the development of power electronic devices, while the development of the switching inverter technology, the two promote each other to promote the switching power supply annual growth rate of more than two digits toward the light, small, thin, low-noise, high reliability, the direction of development of anti-jamming. Switching power supply can be divided into AC / DC and DC / DC two categories, AC / AC DC / AC, such as inverters, DC / DC converter is now modular design technology and production processes at home and abroad have already matured and standardization, and has been recognized by the user, but AC / DC modular, its own characteristics make the modular process, encounter more complex technology and manufacturing process. Hereinafter to illustrate the structure and characteristics of the two types of switching power supply.Self-excited: no external signal source can be self-oscillation, completely self-excited to see it as feedback oscillation circuit of a transformer.Separate excitation: entirely dependent on external sustain oscillations, excited used widely in practical applications. According to the excitation signal structure classification; can be divided into pulse-width-modulated and pulse amplitude modulated two pulse width modulated control the width of the signal is frequency, pulse amplitude modulation control signal amplitude between the same effect are the oscillation frequency to maintain within a certain range to achieve the effect of voltage stability. The winding of the transformer can generally be divided into three types, one group is involved in the oscillation of the primary winding, a group of sustained oscillations in the feedback winding, there is a group of load winding. Such as Shanghai is used in household appliances art technological production of switching power supply, 220V AC bridge rectifier, changing to about 300V DC filter added tothe collector of the switch into the transformer for high frequency oscillation, the feedback winding feedback to the base to maintain the circuit oscillating load winding induction signal, the DC voltage by the rectifier, filter, regulator to provide power to the load. Load winding to provide power at the same time, take up the ability to voltage stability, the principle is the voltage output circuit connected to a voltage sampling device to monitor the output voltage changes, and timely feedback to the oscillator circuit to adjust the oscillation frequency, so as to achieve stable voltage purposes, in order to avoid the interference of the circuit, the feedback voltage back to the oscillator circuit with optocoupler isolation.technology developmentsThe high-frequency switching power supply is the direction of its development, high-frequency switching power supply miniaturization, and switching power supply into the broader field of application, especially in high-tech fields, and promote the development and advancement of the switching power supply, an annual more than two-digit growth rate toward the light, small, thin, low noise, high reliability, the direction of the anti-jamming. Switching power supply can be divided into AC / DC and DC / DC two categories, the DC / DC converter is now modular design technology and production processes at home and abroad have already matured and standardized, and has been recognized by the user, but modular AC / DC, because of its own characteristics makes the modular process, encounter more complex technology and manufacturing process. In addition, the development and application of the switching power supply in terms of energy conservation, resource conservation and environmental protection are of great significance.The switching power supply applications in power electronic devices as diodes, IGBT and MOSFET.SCR switching power supply input rectifier circuit and soft start circuit, a small amount of applications, the GTR drive difficult, low switching frequency, gradually replace the IGBT and MOSFET.Direction of development of the switching power supply is a high-frequency, high reliability, low power, low noise, jamming and modular. Small, thin, and the key technology is the high frequency switching power supply light, so foreign major switching power supply manufacturers have committed to synchronize the development of new intelligent components, in particular, is to improve the secondary rectifier loss, and the power of iron Oxygen materials to increase scientific and technological innovation in order to improve the magnetic properties of high frequency and large magnetic flux density (Bs), and capacitor miniaturization is a key technology. SMT technology allows the switching power supply has made considerable progress, the arrangement of the components in the circuit board on bothsides, to ensure that the light of the switching power supply, a small, thin. High-frequency switching power supply is bound to the traditional PWM switching technology innovation, realization of ZVS, ZCS soft-switching technology has become the mainstream technology of the switching power supply, and a substantial increase in the efficiency of the switching power supply. Indicators for high reliability, switching power supply manufacturers in the United States by reducing the operating current, reducing the junction temperature and other measures to reduce the stress of the device, greatly improve the reliability of products.Modularity is the overall trend of switching power supply, distributed power systems can be composed of modular power supply, can be designed to N +1 redundant power system, and the parallel capacity expansion. For this shortcoming of the switching power supply running noise, separate the pursuit of high frequency noise will also increase, while the use of part of the resonant converter circuit technology to achieve high frequency, in theory, but also reduce noise, but some The practical application of the resonant converter technology, there are still technical problems, it is still a lot of work in this field, so that the technology to be practical.Power electronics technology innovation, switching power supply industry has broad prospects for development. To accelerate the pace of development of the switching power supply industry in China, it must take the road of technological innovation, out of joint production and research development path with Chinese characteristics and contribute to the rapid development of China's national economy.Developments and trends of the switching power supply1955 U.S. Royer (Roger) invented the self-oscillating push-pull transistor single-transformer DC-DC converter is the beginning of the high-frequency conversion control circuit 1957 check race Jen, Sen, invented a self-oscillating push-pull dual transformers, 1964, U.S. scientists canceled frequency transformer in series the idea of switching power supply, the power supply to the size and weight of the decline in a fundamental way. 1969 increased due to the pressure of the high-power silicon transistor, diode reverse recovery time shortened and other components to improve, and finally made a 25-kHz switching power supply.At present, the switching power supply to the small, lightweight and high efficiency characteristics are widely used in a variety of computer-oriented terminal equipment, communications equipment, etc. Almost all electronic equipment is indispensable for a rapid development of today's electronic information industry power mode. Bipolar transistor made of 100kHz, 500kHz power MOS-FET made, though already the practical switching power supply is currently available on the market, but its frequency to be further improved. Toimprove the switching frequency, it is necessary to reduce the switching losses, and to reduce the switching losses, the need for high-speed switch components. However, the switching speed will be affected by the distribution of the charge stored in the inductance and capacitance, or diode circuit to produce a surge or noise. This will not only affect the surrounding electronic equipment, but also greatly reduce the reliability of the power supply itself. Which, in order to prevent the switching Kai - closed the voltage surge, RC or LC buffers can be used, and the current surge can be caused by the diode stored charge of amorphous and other core made of magnetic buffer . However, the high frequency more than 1MHz, the resonant circuit to make the switch on the voltage or current through the switch was a sine wave, which can reduce switching losses, but also to control the occurrence of surges. This switch is called the resonant switch. Of this switching power supply is active, you can, in theory, because in this way do not need to greatly improve the switching speed of the switching losses reduced to zero, and the noise is expected to become one of the high-frequency switching power supply The main ways. At present, many countries in the world are committed to several trillion Hz converter utility.the principle of IntroductionThe switching power supply of the process is quite easy to understand, linear power supplies, power transistors operating in the linear mode and linear power, the PWM switching power supply to the power transistor turns on and off state, in both states, on the power transistor V - security product is very small (conduction, low voltage, large current; shutdown, voltage, current) V oltammetric product / power device is power semiconductor devices on the loss.Compared with the linear power supply, the PWM switching power supply more efficient process is achieved by "chopping", that is cut into the amplitude of the input DC voltage equal to the input voltage amplitude of the pulse voltage. The pulse duty cycle is adjusted by the switching power supply controller. Once the input voltage is cut into the AC square wave, its amplitude through the transformer to raise or lower. Number of groups of output voltage can be increased by increasing the number of primary and secondary windings of the transformer. After the last AC waveform after the rectifier filter the DC output voltage.The main purpose of the controller is to maintain the stability of the output voltage, the course of their work is very similar to the linear form of the controller. That is the function blocks of the controller, the voltage reference and error amplifier can be designed the same as the linear regulator. Their difference lies in the error amplifier output (error voltage) in the drive before the power tube to go through a voltage / pulse-width conversion unit.Switching power supply There are two main ways of working: Forward transformand boost transformation. Although they are all part of the layout difference is small, but the course of their work vary greatly, have advantages in specific applications.the circuit schematicThe so-called switching power supply, as the name implies, is a door, a door power through a closed power to stop by, then what is the door, the switching power supply using SCR, some switch, these two component performance is similar, are relying on the base switch control pole (SCR), coupled with the pulse signal to complete the on and off, the pulse signal is half attentive to control the pole voltage increases, the switch or transistor conduction, the filter output voltage of 300V, 220V rectifier conduction, transmitted through the switching transformer secondary through the transformer to the voltage increase or decrease for each circuit work. Oscillation pulse of negative semi-attentive to the power regulator, base, or SCR control voltage lower than the original set voltage power regulator cut-off, 300V power is off, switch the transformer secondary no voltage, then each circuit The required operating voltage, depends on this secondary road rectifier filter capacitor discharge to maintain. Repeat the process until the next pulse cycle is a half weeks when the signal arrival. This switch transformer is called the high-frequency transformer, because the operating frequency is higher than the 50HZ low frequency. Then promote the pulse of the switch or SCR, which requires the oscillator circuit, we know, the transistor has a characteristic, is the base-emitter voltage is 0.65-0.7V is the zoom state, 0.7V These are the saturated hydraulic conductivity state-0.1V-0.3V in the oscillatory state, then the operating point after a good tune, to rely on the deep negative feedback to generate a negative pressure, so that the oscillating tube onset, the frequency of the oscillating tube capacitor charging and discharging of the length of time from the base to determine the oscillation frequency of the output pulse amplitude, and vice versa on the small, which determines the size of the output voltage of the power regulator. Transformer secondary output voltage regulator, usually switching transformer, single around a set of coils, the voltage at its upper end, as the reference voltage after the rectifier filter, then through the optocoupler, this benchmark voltage return to the base of the oscillating tube pole to adjust the level of the oscillation frequency, if the transformer secondary voltage is increased, the sampling coil output voltage increases, the positive feedback voltage obtained through the optocoupler is also increased, this voltage is applied oscillating tube base, so that oscillation frequency is reduced, played a stable secondary output voltage stability, too small do not have to go into detail, nor it is necessary to understand the fine, such a high-power voltage transformer by switching transmission, separated and after the class returned by sampling the voltage from the opto-coupler pass separated after class, so before the mains voltage, and after the classseparation, which is called cold plate, it is safe, transformers before power is independent, which is called switching power supply.the DC / DC conversionDC / DC converter is a fixed DC voltage transformation into a variable DC voltage, also known as the DC chopper. There are two ways of working chopper, one Ts constant pulse width modulation mode, change the ton (General), the second is the frequency modulation, the same ton to change the Ts, (easy to produce interference). Circuit by the following categories:Buck circuit - the step-down chopper, the average output voltage U0 is less than the input voltage Ui, the same polarity.Boost Circuit - step-up chopper, the average output voltage switching power supply schematic U0 is greater than the input voltage Ui, the same polarity.Buck-Boost circuit - buck or boost chopper, the output average voltage U0 is greater than or less than the input voltage Ui, the opposite polarity, the inductance transmission.Cuk circuit - a buck or boost chopper, the output average voltage U0 is greater than or less than the input voltage Ui, the opposite polarity, capacitance transmission.The above-mentioned non-isolated circuit, the isolation circuit forward circuits, feedback circuit, the half-bridge circuit, the full bridge circuit, push-pull circuit. Today's soft-switching technology makes a qualitative leap in the DC / DC the U.S. VICOR company design and manufacture a variety of ECI soft-switching DC / DC converter, the maximum output power 300W, 600W, 800W, etc., the corresponding power density (6.2 , 10,17) W/cm3 efficiency (80-90)%. A the Japanese Nemic Lambda latest using soft-switching technology, high frequency switching power supply module RM Series, its switching frequency (200 to 300) kHz, power density has reached 27W/cm3 with synchronous rectifier (MOSFETs instead of Schottky diodes ), so that the whole circuit efficiency by up to 90%.AC / DC conversionAC / DC conversion will transform AC to DC, the power flow can be bi-directional power flow by the power flow to load known as the "rectification", referred to as "active inverter power flow returned by the load power. AC / DC converter input 50/60Hz AC due must be rectified, filtered, so the volume is relatively large filter capacitor is essential, while experiencing safety standards (such as UL, CCEE, etc.) and EMC Directive restrictions (such as IEC, FCC, CSA) in the AC input side must be added to the EMC filter and use meets the safety standards of the components, thus limiting the miniaturization of the volume of AC / DC power, In addition, due to internal frequency, high voltage, current switching, making the problem difficult to solve EMC also high demands on the internal high-density mountingcircuit design, for the same reason, the high voltage, high current switch makes power supply loss increases, limiting the AC / DC converter modular process, and therefore must be used to power system optimal design method to make it work efficiency to reach a certain level of satisfaction.AC / DC conversion circuit wiring can be divided into half-wave circuit, full-wave circuit. Press the power phase can be divided into single-phase three-phase, multiphase. Can be divided into a quadrant, two quadrant, three quadrants, four-quadrant circuit work quadrant.he selection of the switching power supplySwitching power supply input on the anti-jamming performance, compared to its circuit structure characteristics (multi-level series), the input disturbances, such as surge voltage is difficult to pass on the stability of the output voltage of the technical indicators and linear power have greater advantages, the output voltage stability up to (0.5)%. Switching power supply module as an integrated power electronic devices should be selected。
本科毕业设计(论文)英文翻译模板
本科毕业设计（论文）英文翻译论文标题(中文)学院******姓名***专业*******班级**********大气探测2班学号*************** 大气探测、信处、两个专业填写电子信息工程。
生物医学工程、电子信息科学与技术、雷电防护科学与技术As its name implies, region growing is a procedure that groups pixels or subregions into larger regions based on predefined criteria. The basic approach is to start with a set of “seed ” points and from these grow regions by appending to each seed those gray level or color).be used to assignpixels to regions during the centroid of these clusters can be used as seeds.… … …左右手共面波导的建模与带通滤波器设计速发展之势，而它的出现却是源于上世纪本研究提出了一种新型混合左右手（CPW ）的独特功能。
目前这种有效电长度为0°的新型混合左右手共面波导（CRLH CPW ）谐振器正在兴起，这种谐振器工作在5GHz 时的体积比常规结构的谐振器缩减小49.1%。
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英文资料翻译Image processing is not a one step process．We are able to distinguish between several steps which must be performed one after the other until we can extract the data of interest from the observed scene．In this way a hierarchical processing scheme is built up as sketched in Fig．The figure gives an overview of the different phases of image processing.Image processing begins with the capture of an image with a suitable，not necessarily optical，acquisition system．In a technical or scientific application，we may choose to select an appropriate imaging system．Furthermore，we can set up the illumination system，choose the best wavelength range，and select other options to capture the object feature of interest in the best way in an image．Once the image is sensed，it must be brought into a form that can be treated with digital computers.This process is called digitization.With the problems of traffic are more and more serious. Thus Intelligent Transport System (ITS) comes out. The subject of the automatic recognition of license plate is one of the most significant subjects that are improved from the connection of computer vision and pattern recognition. The image imputed to the computer is disposed and analyzed in order to localization the position and recognition the characters on the license plate express these characters in text string form The license plate recognition system (LPSR) has important application in ITS. In LPSR, the first step is for locating the license plate in the captured image which is very important for character recognition. The recognition correction rate of license plate is governed by accurate degree of license plate location. In this paper, several of methods in image manipulation are compared and analyzed, then come out the resolutions for localization of the car plate. The experiences show that the good result has been got with these methods. The methods based on edge map and frequency analysis is used in the process of the localization of the license plate, that is to say, extracting the characteristics of the license plate in the car images after being checked up forthe edge, and then analyzing and processing until the probably area of license plate is extracted.The automated license plate location is a part of the image processing ,it’s also an important part in the intelligent traffic system．It is the key step in the Vehicle License Plate Recognition(LPR).A method for the recognition of images of different backgrounds and different illuminations is proposed in the paper.the upper and lower borders are determined through the gray variation regulation of the character distribution.The left and right borders are determined through the black-white variation of the pixels in every row.The first steps of digital processing may include a number of different operations and are known as image processing．If the sensor has nonlinear characteristics, these need to be corrected．Likewise，brightness and contrast of the image may require improvement．Commonly，too，coordinate transformations are needed to restore geometrical distortions introduced during image formation．Radiometric and geometric corrections are elementary pixel processing operations．It may be necessary to correct known disturbances in the image，for instance caused by a defocused optics，motion blur，errors in the sensor，or errors in the transmission of image signals．We also deal with reconstruction techniques which are required with many indirect imaging techniques such as tomography that deliver no direct image.A whole chain of processing steps is necessary to analyze and identify objects．First，adequate filtering procedures must be applied in order to distinguish the objects of interest from other objects and the background．Essentially，from an image（or several images），one or more feature images are extracted．The basic tools for this task are averaging and edge detection and the analysis of simple neighborhoods and complex patterns known as texture in image processing．An important feature of an object is also its motion．Techniques to detect and determine motion are necessary．Then the object has to be separated from the background．This means that regions of constant features and discontinuities must be identified．This process leads to alabel image．Now that we know the exact geometrical shape of the object，we can extract further information such as the mean gray value，the area，perimeter，and other parameters for the form of the object[3]．These parameters can be used to classify objects．This is an important step in many applications of image processing，as the following examples show：In a satellite image showing an agricultural area，we would like to distinguish fields with different fruits and obtain parameters to estimate their ripeness or to detect damage by parasites．There are many medical applications where the essential problem is to detect pathologi-al changes．A classic example is the analysis of aberrations in chromosomes．Character recognition in printed and handwritten text is another example which has been studied since image processing began and still poses significant difficulties．You hopefully do more，namely try to understand the meaning of what you are reading．This is also the final step of image processing，where one aims to understand the observed scene．We perform this task more or less unconsciously whenever we use our visual system．We recognize people，we can easily distinguish between the image of a scientific lab and that of a living room，and we watch the traffic to cross a street safely．We all do this without knowing how the visual system works．For some times now，image processing and computer-graphics have been treated as two different areas．Knowledge in both areas has increased considerably and more complex problems can now be treated．Computer graphics is striving to achieve photorealistic computer-generated images of three-dimensional scenes，while image processing is trying to reconstruct one from an image actually taken with a camera．In this sense，image processing performs the inverse procedure to that of computer graphics．We start with knowledge of the shape and features of an object—at the bottom of Fig. and work upwards until we get a two-dimensional image．To handle image processing or computer graphics，we basically have to work from the same knowledge．We need to know the interaction between illumination and objects，how a three-dimensional scene is projected onto an image plane，etc．There are still quite a few differences between an image processing and a graphics workstation．But we can envisage that，when the similarities and interrelations between computergraphics and image processing are better understood and the proper hardware is developed，we will see some kind of general-purpose workstation in the future which can handle computer graphics as well as image processing tasks[5]．The advent of multimedia，i. e. ，the integration of text，images，sound，and movies，will further accelerate the unification of computer graphics and image processing.In January 1980 Scientific American published a remarkable image called Plume2，the second of eight volcanic eruptions detected on the Jovian moon by the spacecraft Voyager 1 on 5 March 1979．The picture was a landmark image in interplanetary exploration—the first time an erupting volcano had been seen in space．It was also a triumph for image processing．Satellite imagery and images from interplanetary explorers have until fairly recently been the major users of image processing techniques，where a computer image is numerically manipulated to produce some desired effect-such as making a particular aspect or feature in the image more visible.Image processing has its roots in photo reconnaissance in the Second World War where processing operations were optical and interpretation operations were performed by humans who undertook such tasks as quantifying the effect of bombing raids．With the advent of satellite imagery in the late 1960s，much computer-based work began and the color composite satellite images，sometimes startlingly beautiful, have become part of our visual culture and the perception of our planet．Like computer graphics，it was until recently confined to research laboratories which could afford the expensive image processing computers that could cope with the substantial processing overheads required to process large numbers of high-resolution images．With the advent of cheap powerful computers and image collection devices like digital cameras and scanners，we have seen a migration of image processing techniques into the public domain．Classical image processing techniques are routinely employed bygraphic designers to manipulate photographic and generated imagery，either to correct defects，change color and so on or creatively to transform the entire look of an image by subjecting it to some operation such as edge enhancement.A recent mainstream application of image processing is the compression of images—either for transmission across the Internet or the compression of moving video images in video telephony and video conferencing．Video telephony is one of the current crossover areas that employ both computer graphics and classical image processing techniques to try to achieve very high compression rates．All this is part of an inexorable trend towards the digital representation of images．Indeed that most powerful image form of the twentieth century—the TV image—is also about to be taken into the digital domain．Image processing is characterized by a large number of algorithms that are specific solutions to specific problems．Some are mathematical or context-independent operations that are applied to each and every pixel．For example，we can use Fourier transforms to perform image filtering operations．Others are“algorithmic”—we may use a complicated recursive strategy to find those pixels that constitute the edges in an image．Image processing operations often form part of a computer vision system．The input image may be filtered to highlight or reveal edges prior to a shape detection usually known as low-level operations．In computer graphics filtering operations are used extensively to avoid abasing or sampling artifacts．中文翻译图像处理不是一步就能完成的过程。