图像采集技术外文翻译参考文献
数字图像处理外文翻译参考文献
数字图像处理外文翻译参考文献(文档含中英文对照即英文原文和中文翻译)原文：Application Of Digital Image Processing In The MeasurementOf Casting Surface RoughnessAhstract- This paper presents a surface image acquisition system based on digital image processing technology. The image acquired by CCD is pre-processed through the procedure of image editing, image equalization, the image binary conversation and feature parameters extraction to achieve casting surface roughness measurement. The three-dimensional evaluation method is taken to obtain the evaluation parametersand the casting surface roughness based on feature parameters extraction. An automatic detection interface of casting surface roughness based on MA TLAB is compiled which can provide a solid foundation for the online and fast detection of casting surface roughness based on image processing technology.Keywords-casting surface; roughness measurement; image processing; feature parametersⅠ.INTRODUCTIONNowadays the demand for the quality and surface roughness of machining is highly increased, and the machine vision inspection based on image processing has become one of the hotspot of measuring technology in mechanical industry due to their advantages such as non-contact, fast speed, suitable precision, strong ability of anti-interference, etc [1,2]. As there is no laws about the casting surface and the range of roughness is wide, detection parameters just related to highly direction can not meet the current requirements of the development of the photoelectric technology, horizontal spacing or roughness also requires a quantitative representation. Therefore, the three-dimensional evaluation system of the casting surface roughness is established as the goal [3,4], surface roughness measurement based on image processing technology is presented. Image preprocessing is deduced through the image enhancement processing, the image binary conversation. The three-dimensional roughness evaluation based on the feature parameters is performed . An automatic detection interface of casting surface roughness based on MA TLAB is compiled which provides a solid foundation for the online and fast detection of casting surface roughness.II. CASTING SURFACE IMAGE ACQUISITION SYSTEMThe acquisition system is composed of the sample carrier, microscope, CCD camera, image acquisition card and the computer. Sample carrier is used to place tested castings. According to the experimental requirements, we can select a fixed carrier and the sample location can be manually transformed, or select curing specimens and the position of the sampling stage can be changed. Figure 1 shows the whole processing procedure.,Firstly,the detected castings should be placed in the illuminated backgrounds as far as possible, and then through regulating optical lens, setting the CCD camera resolution and exposure time, the pictures collected by CCD are saved to computer memory through the acquisition card. The image preprocessing and feature value extraction on casting surface based on corresponding software are followed. Finally the detecting result is output.III. CASTING SURFACE IMAGE PROCESSINGCasting surface image processing includes image editing, equalization processing, image enhancement and the image binary conversation,etc. The original and clipped images of the measured casting is given in Figure 2. In which a) presents the original image and b) shows the clipped image.A.Image EnhancementImage enhancement is a kind of processing method which can highlight certain image information according to some specific needs and weaken or remove some unwanted informations at the same time[5].In order to obtain more clearly contour of the casting surface equalization processing of the image namely the correction of the image histogram should be pre-processed before image segmentation processing. Figure 3 shows the original grayscale image and equalization processing image and their histograms. As shown in the figure, each gray level of the histogram has substantially the same pixel point and becomes more flat after gray equalization processing. The image appears more clearly after the correction and the contrast of the image is enhanced.Fig.2 Casting surface imageFig.3 Equalization processing imageB. Image SegmentationImage segmentation is the process of pixel classification in essence. It is a very important technology by threshold classification. The optimal threshold is attained through the instmction thresh = graythresh (II). Figure 4 shows the image of the binary conversation. The gray value of the black areas of the Image displays the portion of the contour less than the threshold (0.43137), while the white area shows the gray value greater than the threshold. The shadows and shading emerge in the bright region may be caused by noise or surface depression.Fig4 Binary conversationIV. ROUGHNESS PARAMETER EXTRACTIONIn order to detect the surface roughness, it is necessary to extract feature parameters of roughness. The average histogram and variance are parameters used to characterize the texture size of surface contour. While unit surface's peak area is parameter that can reflect the roughness of horizontal workpiece.And kurtosis parameter can both characterize the roughness of vertical direction and horizontal direction. Therefore, this paper establisheshistogram of the mean and variance, the unit surface's peak area and the steepness as the roughness evaluating parameters of the castings 3D assessment. Image preprocessing and feature extraction interface is compiled based on MATLAB. Figure 5 shows the detection interface of surface roughness. Image preprocessing of the clipped casting can be successfully achieved by this software, which includes image filtering, image enhancement, image segmentation and histogram equalization, and it can also display the extracted evaluation parameters of surface roughness.Fig.5 Automatic roughness measurement interfaceV. CONCLUSIONSThis paper investigates the casting surface roughness measuring method based on digital Image processing technology. The method is composed of image acquisition, image enhancement, the image binary conversation and the extraction of characteristic parameters of roughness casting surface. The interface of image preprocessing and the extraction of roughness evaluation parameters is compiled by MA TLAB which can provide a solid foundation for the online and fast detection of casting surface roughness.REFERENCE[1] Xu Deyan, Lin Zunqi. The optical surface roughness research pro gress and direction[1]. Optical instruments 1996, 18 (1): 32-37.[2] Wang Yujing. Turning surface roughness based on image measurement [D]. Harbin:Harbin University of Science and Technology[3] BRADLEY C. Automated surface roughness measurement[1]. The InternationalJournal of Advanced Manufacturing Technology ,2000,16(9) :668-674.[4] Li Chenggui, Li xing-shan, Qiang XI-FU 3D surface topography measurement method[J]. Aerospace measurement technology, 2000, 20(4): 2-10.[5] Liu He. Digital image processing and application [ M]. China Electric Power Press,2005译文：数字图像处理在铸件表面粗糙度测量中的应用摘要—本文提出了一种表面图像采集基于数字图像处理技术的系统。
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。
外文文献及翻译DigitalImageProcessingandEdgeDetection数字图像处理与边缘检测
Digital Image Processing and Edge DetectionDigital Image ProcessingInterest in digital image processing methods stems from two principal applica- tion areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for au- tonomous 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, pels, 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 a single 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 highlevel processes. Low-level processes involve primitive opera- tions such as imagepreprocessing 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 ., edges, contours, and the identity of individual objects). Finally, higherlevel 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 ., 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, es- pecially 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 aregrouped 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 the other.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 a n 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 transmi 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 file extensions, such as the jpg file extension 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 long way 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 bound- aryof a region ., 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 ., “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 Fig2 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 sim- ple as detailing regions of an image where the information 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 con- nection 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 Fig2 above by the use of double-headed arrows between the processing modules and the knowledge base, as op- posed to single-headed arrows linking the processing modules.Edge detectionEdge detection is a terminology in and , particularly in the areas of and , to refer to which aim at identifying points in a at which the changes sharply or more formally has point and line detection certainly are important in any discussion on segmentation,edge dectection 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: blur caused by a finite and finite ; 2. caused by shadows created by light sources of non-zero radius; 3. at a smooth object edge; or in the vicinity of object edges.A typical edge might for instance be the border between a block of red color and a blockof yellow. In contrast a (as can be extracted by a ) can be a small number of pixels of a different color on an otherwise unchanging background. For a line, there may therefore 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.5 76 4 152 148 149If 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 , 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 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 or the zero-crossings of a non-linear differential expression, as will be described in the section on following below. As a pre-processing step to edge detection, a smoothing stage, typically Gaussian smoothing, is almost always applied (see also ).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 , 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 with . 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 thresholding parameters, 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 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 we are dealing with local computations,the method of choice to determine whether a value is “significant” or not id to use a 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 term edge segment generally is used if the edge is short in relation to the dimensions of the key problem in segmentation is to assemble edge segments into longer 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 definition of an edge in this case is the same as is important to note that these definitions do not guarantee success in finding edge in an simply give us a formalism to look for derivatives in an image are computed using the derivatives are obtained using the Laplacian.数字图像处理与边缘检测数字图像处理数字图像处理方法的研究源于两个主要应用领域：其一是为了便于人们分析而对图像信息进行改进：其二是为使机器自动理解而对图像数据进行存储、传输及显示。
外文参考文献及翻译稿的要求与格式
百度文库- 让每个人平等地提升自我！外文参考文献及翻译稿的要求及格式一、外文参考文献的要求1、外文原稿应与本研究项目接近或相关联；2、外文原稿可选择相关文章或节选章节，正文字数不少于1500字。
3、格式：外文文献左上角标注“外文参考资料”字样，小四宋体。
1.5倍行距。
标题：三号，Times New Roman字体加粗，居中，行距1.5倍。
段前段后空一行。
作者（居中）及正文：小四号，Times New Roman字体，首行空2字符。
4、A4纸统一打印。
二、中文翻译稿1、中文翻译稿要与外文文献匹配，翻译要正确；2、中文翻译稿另起一页；3、格式：左上角标“中文译文”，小四宋体。
标题：宋体三号加粗居中，行距1.5倍。
段前、段后空一行。
作者（居中）及正文：小四号宋体，数字等Times New Roman字体，1.5倍行距，首行空2字符。
正文字数1500左右。
4、A4纸统一打印。
格式范例如后所示。
百度文库 - 让每个人平等地提升自我！外文参考文献Implementation of internal controls of small andmedium-sized pow erStephen Ryan The enterprise internal control carries out the strength to refer to the enterprise internal control system execution ability and dynamics, it is the one whole set behavior and the technical system, is unique competitive advantage which the enterprise has; Is a series of …………………………标题：三号，Times New Roman字体加粗，居中，行距1.5倍。
医学影像学英文文献
医学影像学英文文献英文回答：Within the realm of medical imaging, sophisticated imaging techniques empower healthcare professionals with the ability to visualize and comprehend anatomical structures and physiological processes in the human body. These techniques are instrumental in diagnosing diseases, guiding therapeutic interventions, and monitoring treatment outcomes.Computed tomography (CT) and magnetic resonance imaging (MRI) are two cornerstone imaging modalities widely employed in medical practice. CT utilizes X-rays and advanced computational algorithms to generate cross-sectional images of the body, providing detailed depictions of bones, soft tissues, and blood vessels. MRI, on the other hand, harnesses the power of powerful magnets and radiofrequency waves to create intricate images that excel in showcasing soft tissue structures, including the brain,spinal cord, and internal organs.Positron emission tomography (PET) and single-photon emission computed tomography (SPECT) are nuclear medicine imaging techniques that involve the administration of radioactive tracers into the body. These tracers accumulate in specific organs or tissues, enabling the visualization and assessment of metabolic processes and disease activity. PET is particularly valuable in oncology, as it can detect the presence and extent of cancerous lesions.Ultrasound, also known as sonography, utilizes high-frequency sound waves to produce images of internal structures. It is a versatile technique commonly employed in obstetrics, cardiology, and abdominal imaging. Ultrasound offers real-time visualization, making it ideal for guiding procedures such as biopsies and injections.Interventional radiology is a specialized field that combines imaging guidance with minimally invasive procedures. Interventional radiologists utilize imaging techniques to precisely navigate catheters and otherinstruments through the body, enabling the diagnosis and treatment of conditions without the need for open surgery. This approach offers reduced invasiveness and faster recovery times compared to traditional surgical interventions.Medical imaging has revolutionized healthcare by providing invaluable insights into the human body. The ability to visualize anatomical structures andphysiological processes in exquisite detail has transformed the practice of medicine, leading to more accurate diagnoses, targeted treatments, and improved patient outcomes.中文回答：医学影像学是现代医学不可或缺的一部分，它利用各种成像技术对人体的解剖结构和生理过程进行可视化和理解，在疾病诊断、治疗方案制定和治疗效果评估中发挥着至关重要的作用。
大数据外文翻译参考文献综述
大数据外文翻译参考文献综述(文档含中英文对照即英文原文和中文翻译)原文：Data Mining and Data PublishingData mining is the extraction of vast interesting patterns or knowledge from huge amount of data. The initial idea of privacy-preserving data mining PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed even to the partyrunning the algorithm. In contrast, privacy-preserving data publishing (PPDP) may not necessarily be tied to a specific data mining task, and the data mining task may be unknown at the time of data publishing. PPDP studies how to transform raw data into a version that is immunized against privacy attacks but that still supports effective data mining tasks. Privacy-preserving for both data mining (PPDM) and data publishing (PPDP) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. One well studied approach is the k-anonymity model [1] which in turn led to other models such as confidence bounding, l-diversity, t-closeness, (α,k)-anonymity, etc. In particular, all known mechanisms try to minimize information loss and such an attempt provides a loophole for attacks. The aim of this paper is to present a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explain their effects on Data Privacy.Although data mining is potentially useful, many data holders are reluctant to provide their data for data mining for the fear of violating individual privacy. In recent years, study has been made to ensure that the sensitive information of individuals cannot be identified easily.Anonymity Models, k-anonymization techniques have been the focus of intense research in the last few years. In order to ensure anonymization of data while at the same time minimizing the informationloss resulting from data modifications, everal extending models are proposed, which are discussed as follows.1.k-Anonymityk-anonymity is one of the most classic models, which technique that prevents joining attacks by generalizing and/or suppressing portions of the released microdata so that no individual can be uniquely distinguished from a group of size k. In the k-anonymous tables, a data set is k-anonymous (k ≥ 1) if each record in the data set is in- distinguishable from at least (k . 1) other records within the same data set. The larger the value of k, the better the privacy is protected. k-anonymity can ensure that individuals cannot be uniquely identified by linking attacks.2. Extending ModelsSince k-anonymity does not provide sufficient protection against attribute disclosure. The notion of l-diversity attempts to solve this problem by requiring that each equivalence class has at least l well-represented value for each sensitive attribute. The technology of l-diversity has some advantages than k-anonymity. Because k-anonymity dataset permits strong attacks due to lack of diversity in the sensitive attributes. In this model, an equivalence class is said to have l-diversity if there are at least l well-represented value for the sensitive attribute. Because there are semantic relationships among the attribute values, and different values have very different levels of sensitivity. Afteranonymization, in any equivalence class, the frequency (in fraction) of a sensitive value is no more than α.3. Related Research AreasSeveral polls show that the public has an in- creased sense of privacy loss. Since data mining is often a key component of information systems, homeland security systems, and monitoring and surveillance systems, it gives a wrong impression that data mining is a technique for privacy intrusion. This lack of trust has become an obstacle to the benefit of the technology. For example, the potentially beneficial data mining re- search project, Terrorism Information Awareness (TIA), was terminated by the US Congress due to its controversial procedures of collecting, sharing, and analyzing the trails left by individuals. Motivated by the privacy concerns on data mining tools, a research area called privacy-reserving data mining (PPDM) emerged in 2000. The initial idea of PPDM was to extend traditional data mining techniques to work with the data modified to mask sensitive information. The key issues were how to modify the data and how to recover the data mining result from the modified data. The solutions were often tightly coupled with the data mining algorithms under consideration. In contrast, privacy-preserving data publishing (PPDP) may not necessarily tie to a specific data mining task, and the data mining task is sometimes unknown at the time of data publishing. Furthermore, some PPDP solutions emphasize preserving the datatruthfulness at the record level, but PPDM solutions often do not preserve such property. PPDP Differs from PPDM in Several Major Ways as Follows ：1) PPDP focuses on techniques for publishing data, not techniques for data mining. In fact, it is expected that standard data mining techniques are applied on the published data. In contrast, the data holder in PPDM needs to randomize the data in such a way that data mining results can be recovered from the randomized data. To do so, the data holder must understand the data mining tasks and algorithms involved. This level of involvement is not expected of the data holder in PPDP who usually is not an expert in data mining.2) Both randomization and encryption do not preserve the truthfulness of values at the record level; therefore, the released data are basically meaningless to the recipients. In such a case, the data holder in PPDM may consider releasing the data mining results rather than the scrambled data.3) PPDP primarily “anonymizes” the data by hiding the identity of record owners, whereas PPDM seeks to directly hide the sensitive data. Excellent surveys and books in randomization and cryptographic techniques for PPDM can be found in the existing literature. A family of research work called privacy-preserving distributed data mining (PPDDM) aims at performing some data mining task on a set of private databasesowned by different parties. It follows the principle of Secure Multiparty Computation (SMC), and prohibits any data sharing other than the final data mining result. Clifton et al. present a suite of SMC operations, like secure sum, secure set union, secure size of set intersection, and scalar product, that are useful for many data mining tasks. In contrast, PPDP does not perform the actual data mining task, but concerns with how to publish the data so that the anonymous data are useful for data mining. We can say that PPDP protects privacy at the data level while PPDDM protects privacy at the process level. They address different privacy models and data mining scenarios. In the field of statistical disclosure control (SDC), the research works focus on privacy-preserving publishing methods for statistical tables. SDC focuses on three types of disclosures, namely identity disclosure, attribute disclosure, and inferential disclosure. Identity disclosure occurs if an adversary can identify a respondent from the published data. Revealing that an individual is a respondent of a data collection may or may not violate confidentiality requirements. Attribute disclosure occurs when confidential information about a respondent is revealed and can be attributed to the respondent. Attribute disclosure is the primary concern of most statistical agencies in deciding whether to publish tabular data. Inferential disclosure occurs when individual information can be inferred with high confidence from statistical information of the published data.Some other works of SDC focus on the study of the non-interactive query model, in which the data recipients can submit one query to the system. This type of non-interactive query model may not fully address the information needs of data recipients because, in some cases, it is very difficult for a data recipient to accurately construct a query for a data mining task in one shot. Consequently, there are a series of studies on the interactive query model, in which the data recipients, including adversaries, can submit a sequence of queries based on previously received query results. The database server is responsible to keep track of all queries of each user and determine whether or not the currently received query has violated the privacy requirement with respect to all previous queries. One limitation of any interactive privacy-preserving query system is that it can only answer a sublinear number of queries in total; otherwise, an adversary (or a group of corrupted data recipients) will be able to reconstruct all but 1 . o(1) fraction of the original data, which is a very strong violation of privacy. When the maximum number of queries is reached, the query service must be closed to avoid privacy leak. In the case of the non-interactive query model, the adversary can issue only one query and, therefore, the non-interactive query model cannot achieve the same degree of privacy defined by Introduction the interactive model. One may consider that privacy-reserving data publishing is a special case of the non-interactivequery model.This paper presents a survey for most of the common attacks techniques for anonymization-based PPDM & PPDP and explains their effects on Data Privacy. k-anonymity is used for security of respondents identity and decreases linking attack in the case of homogeneity attack a simple k-anonymity model fails and we need a concept which prevent from this attack solution is l-diversity. All tuples are arranged in well represented form and adversary will divert to l places or on l sensitive attributes. l-diversity limits in case of background knowledge attack because no one predicts knowledge level of an adversary. It is observe that using generalization and suppression we also apply these techniques on those attributes which doesn’t need th is extent of privacy and this leads to reduce the precision of publishing table. e-NSTAM (extended Sensitive Tuples Anonymity Method) is applied on sensitive tuples only and reduces information loss, this method also fails in the case of multiple sensitive tuples.Generalization with suppression is also the causes of data lose because suppression emphasize on not releasing values which are not suited for k factor. Future works in this front can include defining a new privacy measure along with l-diversity for multiple sensitive attribute and we will focus to generalize attributes without suppression using other techniques which are used to achieve k-anonymity because suppression leads to reduce the precision ofpublishing table.译文：数据挖掘和数据发布数据挖掘中提取出大量有趣的模式从大量的数据或知识。
人脸识别文献
人脸识别文献人脸识别技术在当今社会中得到了广泛的应用，其应用领域涵盖了安全监控、人脸支付、人脸解锁等多个领域。
为了了解人脸识别技术的发展，下面就展示一些相关的参考文献。
1. 《Face Recognition: A Literature Survey》- 作者: Rabia Jafri, Shehzad Tanveer, and Mubashir Ahmad这篇综述性文献回顾了人脸识别领域的相关研究，包括了人脸检测、特征提取、特征匹配以及人脸识别系统的性能评估等。
该文中给出了对不同方法的综合评估，如传统的基于统计、线性判别分析以及近年来基于深度学习的方法。
2. 《Deep Face Recognition: A Survey》- 作者: Mei Wang, Weihong Deng该综述性文献聚焦于深度学习在人脸识别中的应用。
文中详细介绍了深度学习中的卷积神经网络（Convolutional Neural Networks, CNN）以及其在人脸特征学习和人脸识别中的应用。
同时，文中还回顾了一些具有代表性的深度学习人脸识别方法，如DeepFace、VGG-Face以及FaceNet。
3. 《A Survey on Face Recognition: Advances and Challenges》-作者: Anil K. Jain, Arun Ross, and Prabhakar这篇综述性文献回顾了人脸识别技术中的进展和挑战。
文中首先介绍了人脸识别技术的基本概念和流程，然后综述了传统的人脸识别方法和基于机器学习的方法。
此外，该文还介绍了一些面部表情识别、年龄识别和性别识别等相关技术。
4. 《Face Recognition Across Age Progression: A Comprehensive Survey》- 作者: Weihong Deng, Jiani Hu, Jun Guo该综述性文献主要关注跨年龄变化的人脸识别问题。
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。
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.。
数据采集外文文献翻译中英文
数据采集外文文献翻译(含：英文原文及中文译文)文献出处：Txomin Nieva. DATA ACQUISITION SYSTEMS [J]. Computers in Industry, 2013, 4(2):215-237.英文原文DATA ACQUISITION SYSTEMSTxomin NievaData acquisition systems, as the name implies, are products and/or processes used to collect information to document or analyze some phenomenon. In the simplest form, a technician logging the temperature of an oven on a piece of paper is performing data acquisition. As technology has progressed, this type of process has been simplified and made more accurate, versatile, and reliable through electronic equipment. Equipment ranges from simple recorders to sophisticated computer systems. Data acquisition products serve as a focal point in a system, tying together a wide variety of products, such as sensors that indicate temperature, flow, level, or pressure. Some common data acquisition terms are shown below.Data collection technology has made great progress in the past 30 to 40 years. For example, 40 years ago, in a well-known college laboratory, the device used to track temperature rises in bronze made of helium was composed of thermocouples, relays, interrogators, a bundle of papers, anda pencil.Today's university students are likely to automatically process and analyze data on PCs. There are many ways you can choose to collect data. The choice of which method to use depends on many factors, including the complexity of the task, the speed and accuracy you need, the evidence you want, and more. Whether simple or complex, the data acquisition system can operate and play its role.The old way of using pencils and papers is still feasible for some situations, and it is cheap, easy to obtain, quick and easy to start. All you need is to capture multiple channels of digital information (DMM) and start recording data by hand.Unfortunately, this method is prone to errors, slower acquisition of data, and requires too much human analysis. In addition, it can only collect data in a single channel; but when you use a multi-channel DMM, the system will soon become very bulky and clumsy. Accuracy depends on the level of the writer, and you may need to scale it yourself. For example, if the DMM is not equipped with a sensor that handles temperature, the old one needs to start looking for a proportion. Given these limitations, it is an acceptable method only if you need to implement a rapid experiment.Modern versions of the strip chart recorder allow you to retrieve data from multiple inputs. They provide long-term paper records of databecause the data is in graphic format and they are easy to collect data on site. Once a bar chart recorder has been set up, most recorders have enough internal intelligence to operate without an operator or computer. The disadvantages are the lack of flexibility and the relative low precision, often limited to a percentage point. You can clearly feel that there is only a small change with the pen. In the long-term monitoring of the multi-channel, the recorders can play a very good role, in addition, their value is limited. For example, they cannot interact with other devices. Other concerns are the maintenance of pens and paper, the supply of paper and the storage of data. The most important is the abuse and waste of paper. However, recorders are fairly easy to set up and operate, providing a permanent record of data for quick and easy analysis.Some benchtop DMMs offer selectable scanning capabilities. The back of the instrument has a slot to receive a scanner card that can be multiplexed for more inputs, typically 8 to 10 channels of mux. This is inherently limited in the front panel of the instrument. Its flexibility is also limited because it cannot exceed the number of available channels. External PCs usually handle data acquisition and analysis.The PC plug-in card is a single-board measurement system that uses the ISA or PCI bus to expand the slot in the PC. They often have a reading rate of up to 1000 per second. 8 to 16 channels are common, and the collected data is stored directly in the computer and then analyzed.Because the card is essentially a part of the computer, it is easy to establish the test. PC-cards are also relatively inexpensive, partly because they have since been hosted by PCs to provide energy, mechanical accessories, and user interfaces. Data collection optionsOn the downside, the PC plug-in cards often have a 12-word capacity, so you can't detect small changes in the input signal. In addition, the electronic environment within the PC is often susceptible to noise, high clock rates, and bus noise. The electronic contacts limit the accuracy of the PC card. These plug-in cards also measure a range of voltages. To measure other input signals, such as voltage, temperature, and resistance, you may need some external signal monitoring devices. Other considerations include complex calibrations and overall system costs, especially if you need to purchase additional signal monitoring devices or adapt the PC card to the card. Take this into account. If your needs change within the capabilities and limitations of the card, the PC plug-in card provides an attractive method for data collection.Data electronic recorders are typical stand-alone instruments that, once equipped with them, enable the measurement, recording, and display of data without the involvement of an operator or computer. They can handle multiple signal inputs, sometimes up to 120 channels. Accuracy rivals unrivalled desktop DMMs because it operates within a 22 word, 0.004 percent accuracy range. Some data electronic automatic recordershave the ability to measure proportionally, the inspection result is not limited by the user's definition, and the output is a control signal.One of the advantages of using data electronic loggers is their internal monitoring signals. Most can directly measure several different input signals without the need for additional signal monitoring devices. One channel can monitor thermocouples, RTDs, and voltages.Thermocouples provide valuable compensation for accurate temperature measurements. They are typically equipped with multi-channel cards. Built-in intelligent electronic data recorder helps you set the measurement period and specify the parameters for each channel. Once you set it all up, the data electronic recorder will behave like an unbeatable device. The data they store is distributed in memory and can hold 500,000 or more readings.Connecting to a PC makes it easy to transfer data to a computer for further analysis. Most data electronic recorders can be designed to be flexible and simple to configure and operate, and most provide remote location operation options via battery packs or other methods. Thanks to the A/D conversion technology, certain data electronic recorders have a lower reading rate, especially when compared with PC plug-in cards. However, a reading rate of 250 per second is relatively rare. Keep in mind that many of the phenomena that are being measured are physical in nature, such as temperature, pressure, and flow, and there are generallyfewer changes. In addition, because of the monitoring accuracy of the data electron loggers, a large amount of average reading is not necessary, just as they are often stuck on PC plug-in cards.Front-end data acquisition is often done as a module and is typically connected to a PC or controller. They are used in automated tests to collect data, control and cycle detection signals for other test equipment. Send signal test equipment spare parts. The efficiency of the front-end operation is very high, and can match the speed and accuracy with the best stand-alone instrument. Front-end data acquisition works in many models, including VXI versions such as the Agilent E1419A multi-function measurement and VXI control model, as well as a proprietary card elevator. Although the cost of front-end units has been reduced, these systems can be very expensive unless you need to provide high levels of operation, and finding their prices is prohibited. On the other hand, they do provide considerable flexibility and measurement capabilities.Good, low-cost electronic data loggers have the right number of channels (20-60 channels) and scan rates are relatively low but are common enough for most engineers. Some of the key applications include:•product features•Hot die cutting of electronic products•Test of the environmentEnvironmental monitoring•Composition characteristics•Battery testBuilding and computer capacity monitoringA new system designThe conceptual model of a universal system can be applied to the analysis phase of a specific system to better understand the problem and to specify the best solution more easily based on the specific requirements of a particular system. The conceptual model of a universal system can also be used as a starting point for designing a specific system. Therefore, using a general-purpose conceptual model will save time and reduce the cost of specific system development. To test this hypothesis, we developed DAS for railway equipment based on our generic DAS concept model. In this section, we summarize the main results and conclusions of this DAS development.We analyzed the device model package. The result of this analysis is a partial conceptual model of a system consisting of a three-tier device model. We analyzed the equipment project package in the equipment environment. Based on this analysis, we have listed a three-level item hierarchy in the conceptual model of the system. Equipment projects are specialized for individual equipment projects.We analyzed the equipment model monitoring standard package in the equipment context. One of the requirements of this system is the ability to use a predefined set of data to record specific status monitoring reports. We analyzed the equipment project monitoring standard package in the equipment environment. The requirements of the system are: (i) the ability to record condition monitoring reports and event monitoring reports corresponding to the items, which can be triggered by time triggering conditions or event triggering conditions; (ii) the definition of private and public monitoring standards; (iii) Ability to define custom and predefined train data sets. Therefore, we have introduced the "monitoring standards for equipment projects", "public standards", "special standards", "equipment monitoring standards", "equipment condition monitoring standards", "equipment project status monitoring standards and equipment project event monitoring standards, respectively Training item triggering conditions, training item time triggering conditions and training item event triggering conditions are device equipment trigger conditions, equipment item time trigger conditions and device project event trigger condition specialization; and training item data sets, training custom data Sets and trains predefined data sets, which are device project data sets, custom data sets, and specialized sets of predefined data sets.Finally, we analyzed the observations and monitoring reports in the equipment environment. The system's requirement is to recordmeasurements and category observations. In addition, status and incident monitoring reports can be recorded. Therefore, we introduce the concept of observation, measurement, classification observation and monitoring report into the conceptual model of the system.Our generic DAS concept model plays an important role in the design of DAS equipment. We use this model to better organize the data that will be used by system components. Conceptual models also make it easier to design certain components in the system. Therefore, we have an implementation in which a large number of design classes represent the concepts specified in our generic DAS conceptual model. Through an industrial example, the development of this particular DAS demonstrates the usefulness of a generic system conceptual model for developing a particular system.中文译文数据采集系统Txomin Nieva数据采集系统, 正如名字所暗示的, 是一种用来采集信息成文件或分析一些现象的产品或过程。
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 。
人脸识别外文翻译参考文献
人脸识别外文翻译参考文献(文档含中英文对照即英文原文和中文翻译)译文：基于PAC的实时人脸检测和跟踪方法摘要：这篇文章提出了复杂背景条件下，实现实时人脸检测和跟踪的一种方法。
这种方法是以主要成分分析技术为基础的。
为了实现人脸的检测，首先，我们要用一个肤色模型和一些动作信息(如：姿势、手势、眼色)。
然后，使用PAC技术检测这些被检验的区域，从而判定人脸真正的位置。
而人脸跟踪基于欧几里德（Euclidian）距离的，其中欧几里德距离在位于以前被跟踪的人脸和最近被检测的人脸之间的特征空间中。
用于人脸跟踪的摄像控制器以这样的方法工作：利用平衡/（pan/tilt）平台，把被检测的人脸区域控制在屏幕的中央。
这个方法还可以扩展到其他的系统中去，例如电信会议、入侵者检查系统等等。
1.引言视频信号处理有许多应用，例如鉴于通讯可视化的电信会议，为残疾人服务的唇读系统。
在上面提到的许多系统中，人脸的检测喝跟踪视必不可缺的组成部分。
在本文中，涉及到一些实时的人脸区域跟踪[1-3]。
一般来说，根据跟踪角度的不同，可以把跟踪方法分为两类。
有一部分人把人脸跟踪分为基于识别的跟踪喝基于动作的跟踪，而其他一部分人则把人脸跟踪分为基于边缘的跟踪和基于区域的跟踪[4]。
基于识别的跟踪是真正地以对象识别技术为基础的，而跟踪系统的性能是受到识别方法的效率的限制。
基于动作的跟踪是依赖于动作检测技术，且该技术可以被分成视频流（optical flow）的（检测）方法和动作—能量（motion－energy）的（检测）方法。
基于边缘的（跟踪）方法用于跟踪一幅图像序列的边缘，而这些边缘通常是主要对象的边界线。
然而，因为被跟踪的对象必须在色彩和光照条件下显示出明显的边缘变化，所以这些方法会遭遇到彩色和光照的变化。
此外，当一幅图像的背景有很明显的边缘时，（跟踪方法）很难提供可靠的（跟踪）结果。
当前很多的文献都涉及到的这类方法时源于Kass et al.在蛇形汇率波动[5]的成就。
关于结构检测的外国文献
关于结构检测的外国文献结构检测（structure detection）是计算机视觉领域的一个重要研究方向，旨在从图像或视频中检测并识别出不同对象的结构信息。
本文将介绍几篇关于结构检测的外国文献，以展示该领域的研究进展和应用。
1. "Structure extraction in images and videos using deep learning"（使用深度学习在图像和视频中进行结构提取）：这篇文献研究了如何利用深度学习算法来实现图像和视频中的结构提取。
作者提出了一种基于卷积神经网络的方法，通过训练网络来学习图像中的结构信息，并在实验中取得了很好的效果。
2. "Structure detection for 3D reconstruction"（用于三维重建的结构检测）：该文献关注于如何在三维重建过程中进行结构检测。
作者提出了一种基于特征匹配和相机投影的方法，通过分析图像中的特征点和相机参数，实现对场景结构的检测和重建。
实验证明该方法在三维重建中具有较高的精度和鲁棒性。
3. "Structure detection for object recognition"（用于物体识别的结构检测）：该文献研究了如何利用结构检测来提高物体识别的准确性。
作者提出了一种基于边缘检测和支持向量机的方法，通过检测物体的结构信息来辅助物体识别任务。
实验结果表明，该方法在提高物体识别准确性方面取得了显著的效果。
4. "Structure detection for video analysis"（用于视频分析的结构检测）：该文献探讨了如何利用结构检测来进行视频分析。
作者提出了一种基于光流和运动分析的方法，通过检测视频中的结构信息来实现对视频内容的理解和分析。
实验证明该方法在视频分析任务中具有较高的准确性和实时性。
结构检测在计算机视觉领域具有广泛的应用，例如目标检测、场景理解、图像分割等。
imagenet 参考文献引用
很高兴能为您撰写关于imagenet的文章。
imagenet是一个颇具影响力的计算机视觉数据库，它包含了来自世界各地的数百万张图片，每一张图片都被标记和分类。
它的发布对图像识别和深度学习领域产生了深远的影响，并且在学术界和工业界都受到广泛关注。
1. imagenet的创建和影响imagenet数据库创建于2009年，由斯坦福大学的李飞飞教授发起，并在图像识别领域引起了革命性的变化。
它为研究人员提供了一个丰富的数据集，让他们可以训练和测试各种图像识别算法。
在imagenet 的推动下，深度学习技术得到了快速发展，图像识别的准确率大幅提升，以及广泛应用于人脸识别、自动驾驶、医学影像分析等领域。
2. imagenet的重要性imagenet作为图像识别领域的基础数据集，对于深度学习的发展起到了至关重要的作用。
许多研究者利用imagenet进行算法的验证和比较，以此来衡量他们的图像识别模型的准确性。
imagenet还激励了许多学者和工程师在算法优化、网络架构设计等方面的不懈努力，为图像识别技术的进步贡献力量。
3. 我对imagenet的个人观点和理解作为一项重要的计算机视觉工具，imagenet在图像识别领域发挥着巨大的作用。
它促进了深度学习技术的飞速发展，也为研究人员提供了一个广阔的研究评台。
在未来，我相信imagenet会继续对图像识别技术的进步起到重要的推动作用，带来更多的创新和突破。
通过以上的文章撰写，希望能够为您提供一篇有价值的文章，帮助您更深入地了解imagenet这一重要概念。
如果有任何修改意见或其他要求，请随时告诉我。
imagenet作为一个包含数百万张图片的庞大数据库，对计算机视觉和深度学习领域产生了深远的影响。
它的创建和发布不仅推动了图像识别技术的发展，还激发了学术界和工业界的广泛关注和探讨。
通过imagenet，研究人员可以进行算法的验证和比较，提高图像识别模型的准确性，进而应用于各种领域，如人脸识别、自动驾驶、医学影像分析等。
图像处理方面的参考文献
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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。
关于ocr技术的参考文献
关于ocr技术的参考文献关于OCR（光学字符识别）技术的参考文献有很多，我将从不同角度为你介绍一些相关的文献。
首先，如果你对OCR技术的基本原理和发展历史感兴趣，可以参考以下文献：T. M. Breuel的《The OCR Revolution: A Perspective》。
D. Doermann的《The History of OCR》。
S. N. Srihari的《Historical Perspectives on Document Image Analysis and Recognition》。
其次，如果你想了解OCR技术在特定领域的应用，可以参考以下文献：M. Blumenstein和Y. Kong的《Handwriting Recognition: Technologies and Applications》。
K. Roy的《Optical Character Recognition for Indic Scripts》。
S. Marinai和A. Gori的《Handwriting Recognition and Document Analysis: A Comprehensive Reference》。
另外，如果你对OCR技术的最新研究和发展趋势感兴趣，可以参考以下文献：Y. LeCun、L. Bottou、Y. Bengio和P. Haffner的《Gradient-Based Learning Applied to Document Recognition》。
A. Graves、M. Liwicki、S. Fernández和R. Bertolami的《Handwriting Recognition with Large Multilayer Networks》。
D. Karatzas、F. Shafait、S. Uchida、M. Iwamura和L. G.i Bigorda的《ICDAR 2013 Robust Reading Competition》。
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图像采集技术外文翻译参考文献(文档含中英文对照即英文原文和中文翻译)图像采集技术与A VR单片机摘要图像采集系统在各种数字图像应用系统中是不可或缺的部分。
在本文中,我们开发了一种基于A VR单片机的紧凑的图像采集与处理系统。
该系统主要利用A VR单片机ATmega16与低功耗、高性能的数据处理主控制单元。
首先，它完成了CMOS光通过I2C接口接收的相机模块C3088初始化。
然后，它被用来从LCD 上实时显示C3088和获取图像的采集状态。
最后,利用单片机串行通信接口发送数据到电脑, 在经过数据处理显示图像。
硬件电路和软件系统的设计。
关键词：图像采集，单片机，A VR串行通信，视频监控一、简介随着社会的进步和科学的发展,技术与经济,要求更安全的工作和生活环境所倡导的组织和个人对防盗措施都提出了新的要求。
作为一个有效的安全保护手段,在视频监控领域中发挥着重要的作用,公共安全等,已经越来越受到广泛关注[1 - 4]。
目前,视频监督和控制已经进入所有域名,我们几乎每天可以看到它的应用。
图像采集系统在各种数字图像应用系统中是不可或缺的部分。
A VR单片机是基于可编程GSI和计算机技术的大规模集成电路芯片(5 - 9]。
它的快速数据采集和处理功能以及各种功能模块集成在芯片中在各种场合提供丰富便捷的应用程序。
比较CCD、CMOS图像传感器可以将时间序列处理成电路,前端放大器的图像信号和数字部分为一个芯片,因此它的发展是高度强调由行业一向性。
目前,随着技术的发展,噪声的CMOS图像传感器已经有效地改善,并且解决能力明显增强。
CMOS图像传感器由于其低廉的价格,图像质量,高整合度和相对较少的电力消耗将被广泛应用在视频采集领域。
因此,在本文中,我们开发实施的图像数据采集系统是基于A VR单片机的。
程序驱动摄影机C3088[10]通过单片机ATmega16获取原始图像数据, 通过I2C接口的初始化摄像头协议,并实现数据传输,。
该电路具有许多优点如结构简单,方便转移和低CPU占用,它可以降低系统的总成本。
二、系统结构它以数字化和自动化的水准在传统光学采集系统中安装电气部分数据处理。
数据处理单元的原则包括快速数据运算速度,丰富的外围接口和低功耗。
根据这些原则,我们采用A VR单片机与高性能的设计,它可以结合获取的信息数据采集仪在CMOS图像传感器前端显示,其结构示意图见图1。
该系统采用单片机作为微控制器,驱动摄像头通过I2C总线,主要是初始化一些寄存器,组成了图像传感器。
当初始化完成时,相机输出三个符号包括像素时钟信号同步信号和垂直,生的同步信号。
单片机读取原始数据的图像并从相机数据总线通过测试这三个标志信号,暂时存储数据到数据存储器在单片机,然后传输数据到计算机通过串行通信。
图1.系统结构硬件选择器在硬件的实现方面对于系统的整体性能是非常重要的。
我们可以选择单片机具有较高的性价比和速度,我们也可以选择功能强大的DSP,速度快,多个接口和核心芯片,我们也可以选择ARM微处理器。
作为视频采集系统、图像系统中使用的传感器的速度可以由单片机控制。
当我们第一次用51系列单芯片微型计算机在设计过程中 ,我们发现它无法满足数据庞大的吞吐量所需的视频采集。
所以我们使用ATmega16系统中可以满足设计要求的系统。
单片机被广泛应用于爱特梅尔公司的许多领域如工业生产控制、智能仪器、数据采集和家用电器。
这种单片机具有RISC结构。
由于其先进的指令集和单时钟周期指令执行时间。
当它工作达到16 MIPS 16兆赫时,它可以减少能耗之间的冲突加快处理速度。
执行一个指令只需要一个时钟周期，且速度比传统的单片机快很多，所以它可以胜任高速条件下的A/D采样的控制。
但当我们使用DSP，ARM和FPGA／CPLD，它会浪费资源，使系统变得越来越复杂，成本和性能是不合理的。
用于视频捕获设备，无论是CCD或CMOS全部采用光接收元件作为捕获图像的基本措施。
核心的CCD / CMOS光接收组件是一个光接收二极管，产生输出电流时，接收光的照射。
电流的强度对应光的照射强度。
对于周边设备，光接收元件CCD与光接收元件不同，除了光接收二极管。
光接收的前部分还包括一个存储单元，用于控制相邻电荷。
光接收二极管占多数的面积，即有效的受光面积，CCD光接收组件更大，它可以在相同的条件下获得更强的光信号，并使输出相应的电信号更清楚。
由于CMOS图像传感器技术发展很快，每一个光接收元件可以直接集成在CMOS传感器放大器并完成逻辑模数转换。
当光接收二极管接收光的照射而产生的模拟电信号，并模拟电信号放大在光接收元件之前转换成相应的数字信号。
换句话说，在CMOS传感器，每个光接收组件产品都有最终的数字输出。
由于集成度高，体积小，使用方便，内容丰富，图像捕获快速，我们采用CMOS传感器组成的相机模块C3088（OV6620）作为设计的采集设备。
默认的分辨率的摄像头是356×292，所以它非常适合于单芯片微型计算机的操作能力。
该相机模块C3088工作电压为5V，它的引脚20和引脚22与电源连接，和引脚31连接数字地球，与引脚21，引脚15和引脚17连接模拟地球。
数据线连接~ Y0 Y7 PA0 ~ PA7单片机的数据线，和uv0 ~ uv7连接PB0 ~ PB7的单片机，和PCLK与PD2，和超链接与PD3，和垂直同步连接PD4。
SDA和SCL与PC1，接PC0。
现场可编程逻辑器件包括356×292分辨率的图像阵列，模拟信号处理器，双8位模拟数字转换，模拟视频多路转接器，数字格式输出端口，一个模拟视频接口，I2C总线接口及寄存器。
该传感器采用基于完整图像的电子曝光控制算法。
单片机采用RS232串行通信与上位计算机的通信。
其电路结构是非常简单的，它可以保证系统的稳定和满足系统设计要求。
该系统采用RS232连接计算机和视频数据传输。
ATmega16采用8位数据位，1位停止位和0个奇偶校验位，其速度可以达到230400bps，和计算机的串行端口速度仅为115200bps，可以满足系统的设计要求。
由于单片机的电压ttl5v和RS232电压12V，双方需要进行电平转换来识别。
该系统需要与单片机通过MAX232芯片如图2所示连接。
图2.电压匹配电路显示的液晶采用ZT018智能全彩液晶。
这个模块具有基本的绘图功能,采用通常的微型客车作为接口,因此它可以节省开发时间以及方便发展和转移。
它的接口模式包括SPI和I2C,本系统采用I2C接口,只需要两个数据行。
四、软件C3088由CMOS图像传感器OV6620,它的初始化主要取决于通过I2C对内部寄存器的写入操作。
通过软件的初始化设置,视频数字输出可以使用不同的格式并和其他寄存器进行初始化。
因为最初的时钟频率的C3088相机模块是17.73 mhz,当它工作在16位数据输出模式,其PCLK时钟周期是112 ns和当它工作在8位数据输出模式,其PCLK时钟周期是56 ns。
晶体振荡所采用的单片机是16兆赫和单周期是62.5 ns。
所以单片机可以不遵循视频的速度。
它必须通过写寄存器0 x11降低时钟频率PCLK,设置低5位的寄存器在“1”可以减少PCLK 至69.25千赫,可以适合单片机处理较低的速度。
数据输出格式通常的CMOS图像传感器是原始数据输出格式。
由于CMOS 光传感器单元具有三种颜色不同的响应灵敏度，响应是非线性的。
它是相对于亮度，加上材料。
因此，图像传感器的原始数据应校正和补偿。
不同厂家的产品补偿曲线是不同的，所以我们应该设计不同的补偿算法。
现场可编程逻辑器件不仅可以输出的原始数据格式的R，G和B，并将色彩补偿算法在芯片中，并能输出YUV和YCrCb视频输出格式符合CCIR601标准。
相机通过初始化工作在最低频率。
单片机可将数据存储在单片机数据存储器时，它读取原始数据，并将所获得的程序转换为BMP格式的数据，并将数据传送给计算机进行存储和显示。
串行通信是一种通信模式，可以通过比特传输二进制数据，因此传输线所需要的是很少的数量，这是非常适合的分级控制系统，分层控制系统，分布式控制系统和远程通信。
由于分布式控制系统被广泛应用在现代的计算机控制系统，因此它往往需要一个主计算机控制多台下位机，与计算机和单片机成为一个重要的问题之间的通信。
单芯片的微型计算机的程序流程图如图3所示。
计算机程序流程图如图4所示。
图3.程序流程图的数据采集和发送图4.程序流程图的数据接收和处理五、结论视频数据的采集和存储技术日复一日发展很快，识别技术，数字电视和实时监测和控制的行业前景很好。
相机的图像采集系统基于A VR单片机和C3088简化了系统的结构。
该系统具有清晰的图像，可满足实时显示的要求，可广泛应用于工业自动化监控网络视频。
Image Acquisition Technology with A VR Single Chip Microcomputer Xiao ChenDepartment of Electronic Information Engineering,Nanjing University of Information Science and TechnologyNanjing 210044, ChinaAbstract—The image acquisition system is one of indispensable parts in various kinds of digital image application system. In this article, we developed a sort of video camera compact image acquisition and processing system based on A VR single chip microcomputer. The system utilises A VR single chip microcomputer ATmega16 with low power consumption and high performance as the data processing main control unit. Firstly, it completes the initialisation of CMOS light-receiving camera module C3088 through I2C interface. Then it is used to acquire image from C3088 and the acquisition states are displayed on LCD real time. Finally, the single chip microcomputer utilises serial communication interface to send data to the computer, which displays the image after data processing. The hardware circuit and the software programs of the system are designed.Keywords-Image acquisition; single chip microcomputer; AVR; serial communication; video surveillanceI. INTRODUCTIONWith the progress of society and the development of science, technology and economy, the demands for more security in working and living environment has been advocated by both organizations and individuals, which puts forward new requirements for anti-theft measures. As an effective means of security protection, video monitoring plays an important role in fields of public security, etc., and has drawn increasing and extensive attentions [1-4]. At present, Video supervision and control has entered into all domains, and we can see its applications almost everyday. The image acquisition system is one of indispensable parts in various kinds of digital image application system.A VR single chip microcomputer is the integrated chip based on programmable GSI and computer technology [5-9]. Its quick data acquisition and processing function and various function modules integrated in the chip offer abundant conveniences for its applications in various occasions. Comparing with CCD, the CMOS image sensor could integrate the time sequence processing circuit, the front-end amplifier of image signals and digital part into one chip, so its development is highly emphasised by the industry all along. At present, with the development of technology and technique, the yawp of CMOS image sensor has been improved effectively and its resolving capability has been obviously enhanced. CMOS image sensor will be extensively applied in the video acquisition domain because of its cheap price, applied image quality, high integration degree and relatively little power consumption. Therefore, in this article, we develop the implementation program of video data acquisition system based on A VR single chip microcomputer. The program drives the camera C3088 [10] through single chip microcomputer ATmega16 to obtain the original image dataacquired by the camera, and implement data transmission and initialisation of camera through I2C interface protocol. This circuit has many advantages such as simple structure, convenient transfer and low CPU occupation rate, and it can reduce the total cost of the System.II. SYSTEM STRUCTUREIt can realize the digitalization and automatization of leveling to install the electric parts of data process in traditional optical acquisition system. The principles of data processing unit include quick data operation speed,abundant peripheral interfaces and low power consumption.According to these principles, we adopt A VR single chip microcomputer with high performance in the design, which can combine with the information acquired by the data acquisition apparatus CMOS image sensor in the front end, and its structure sketch is seen in Fig. 1. The system adopt single chip microcomputer as the micro-controller to drive the camera through I2C bus, which mainly initializes some registers which composes the image sensor of the camera.When the initialization of camera is completed, the camera outputs three symbol signals including pixel clock, raw synchronous signal and vertical synchronous signal. The single chip microcomputer read the original data of image from the camera data bus through testing these three symbol signals and temporarily stores the data into the data memorizer in single chip microcomputer, and then transmit the data to the computer through serial communication.III. HARDWAREThe selection of apparatus in the implementation of hardware is very important for the performance of the whole system. We can select the single chip microcomputer with high cost performance and high speed, and we can also select the DSP with powerful function, quick speed,multiple interfaces and good stability as the core chip, and we can also select ARM microprocessor. As the video acquisition system, the image sensor speed used in the system can be controlled by the single chip microcomputer.When we first used 51 series ingle chip microcomputer in the design process, we found it couldn't fulfill the throughput of large data needed by the video acquisition.So we use ATmega16 in the system which can fulfill the design requirement of the system. The single chip microcomputer of ATMEL Company is extensively applied in many domains such as industrial production control,intelligent instruments, data acquisition and home electric appliances. This kind of single chip microcomputer has the structure of RISC. Because of its advanced instruction set and single clock period instruction execution time, the performance of A VR single chip microcomputer can achieve 16 MIPS when it works in 16MHz, which can reduce theconflict between power consumption and processing speed. The execution of one instruction only needs one clock cycle, and the speed is much quicker than traditional single chip microcomputer, so it can be competent for the control of A/D sampling under the condition of high speed. But when we use DSP, ARM or FPGA/CPLD, it will waste the resources and make the system become more complex, and the cost performance is not so reasonable.For the video capture equipments,whether CCD or CMOS all adopt the light-receiving component as the basic measure to capture images. The core of CCD/CMOS light receiving component is a light-receiving diode which can produce output current when receiving light irradiation.The intensity of the current is corresponding to the intensity of the irradiation. For the peripheral equipments, the light receiving component of CCD is different to the light receiving component of CMOS, and except for the light receiving diode. The light-receiving component of the former also includes one storage unit which is used to control the neighboring charge. The light-receiving diode occupies most areas, i.e. the effective light-receiving area of the CCD light-receiving component is bigger, and it can receive stronger light signal under same condition, and the corresponding output electric signals are more clear. Because the CMOS image sensor technology develops very quickly, every light-receiving component in CMOS sensor can directly integrate the amplifier and the analog-to-digital conversion logic. When the light-receiving diode receives light irradiation and produces analog electric signals, and the signals are amplified by the amplifier in the light receiving component firstly and then converted into corresponding digital signals. In another words, in the CMOS sensor, every light-receiving component can product final digital output. Because of high integration degree, small volume, convenient use and abundant image content effect captured, we adopt the camera C3088 module composed by CMOS sensor (OV6620) as the acquisition equipment in the design. The default resolving capability of the camera is 356*292, so it is very fit for the single chip microcomputer with low operation ability.The work voltage of the camera module C3088 is 5V, and its pin 20 and pin 22 connect with the power supply, and the pin 31 connects with the digital earth, and the pin 21, pin 15 and pin 17 connect with the analog earth. Data lines Y0~Y7 connect PA0~PA7 of the single chip microcomputer, and data lines UV0~UV7 connect with PB0~PB7 of the single chip microcomputer, and PCLK connects with PD2, and HREF connects with PD3, and VSYNC connects with PD4. SDA connects with PC1, and SCL connects with PC0. OV6620 includes the image array with the resolving capability of 356*292, an analog signal processor, double 8bits analog-to-digital conversion, analog video multiple routes commutator, digital output format port, an analog video port, I2C bus interface and its register. The sensor uses the electric exposal control algorithm based on complete image.The single chip microcomputer selects RS232 serial communication to communicate with the computer. Its circuit structure is very simple, which can ensure the stability of the system and fulfill the design requirement of the system. RS232 is one of communication interfaces on personal computer, and it is the asynchronoustransmission standard interface constituted by the Electronic Industries Association (EIA). This system adopts RS232 to connect with computer and transmit video data. The ATmega16 adopts 8bits data bit, 1bit stop bit and 0 parity bit, and its speed can achieve 230400bps, and speed of the serial port of the computer is only 115200bps, which can fulfill the design requirement of the system.Because the voltage of the SCM is TTL5V and the voltage of the RS232 is -12V, so both sides need level conversion to identify the other. The system needs to connect with single chip microcomputer through the chip MAX232 as shown in Fig. 2.The display of LCD adopts ZT018 intelligent true color LCD. This module possesses basic plotting function which adopts usual microbus as the interface, so it can save development time and offer large convenience for the development and transfer. Its interface modes include SPI and I2C, and this system adopts I2C interface which only needs two data lines.IV. SFOTWAREC3088 is composed by CMOS image sensor OV6620, and its initialization mainly depends on the write-operation to interior registers through I2C. Through the initialization setting of the software, the video digital output can use different formats and initialize other registers. Because the initial clock frequency of the C3088 camera module is 17.73MHz, when it works in the 16bits data output mode, its PCLK clock cycle is 112ns and when it works in the 8bits data output mode, its PCLK clock cycle is 56ns. The crystal oscillation adopted by the single chip microcomputer is 16MHz and the single order cycle is 62.5ns. So the single chip microcomputer can not follow the speed of the video. It must reduce the PCLK clock frequency through writing the register 0x11, and to set the low 5bits of the register in "1" can reduce PCLK to 69.25KHz, which can fit for the single chip microcomputer processing with low speed.The data output format of the usual CMOS image sensor is the original data output format. Because the CMOS optical sensor unit has different response sensitivities to three sorts of color, and the response is nonlinear.It is relative to the brightness, plus and materials. So the original data of the image sensor should be emendated and compensated. The product compensation curves of different factoriesare different, so we should design different compensation algorithms. OV6620 can not only output original data formats of R, G and B, and integrate color compensate algorithms in the chip, and it can output the video output formats of YUV and YCrCb according with the standard of CCIR601.The camera works in the lowest frequency through initialization. The single chip microcomputer can store the data in the data memorizer of single chip microcomputer when it read a raw of data, and it converts the data acquired by the program into the format of BMP, and transmits the data to the computer for storage and display.The serial communication is a sort of communication mode which can transmit binary system data by bit, so the quantity of the transmission lines needed by it is very few, and it is very fit for grading control system, layer-division control system, distributed control system and remote communication. Because the distributed control system is extensively applied in the modern computer control system, so it often needs one main computer to control multiple slave-computers, and the communication between the computer and single chip microcomputer becomes into an important problem. The program flow chart of single chip microcomputer is shown in Fig. 3. The program flow chart of computer is shown in Fig. 4.V. CONCLUSIONThe acquisition and storage of video data is the accidence of the video technology, and in the day that the video technology develops very quickly, the identification technology, digital TV and real-time supervision and control are the industries with very good foreground. The camera image acquisition system based on A VR single chip microcomputer and C3088 simplifies the structure of the system. The system has clear image, and it can fulfill the requirement of real-time display and be extensively applied in network video and industrial automatic supervision. ACKNOWLEDGMENTThe work is supported by the Basic Research Program(Natural Science Foundation) of Jiangsu Province of China(Granted No. BK2007601), "Qing Lan Gong Cheng"program of Jiangsu Province of China and the Natural Science Foundation of Jiangsu Higher Education Institutions of China (Granted No. 06KJB510048).REFERENCES[1] X Ji, , Z Wei and Y Feng, Effective vehicle detection technique fortraffic surveillance systems, Journal of Visual Communication andImage Representation, 2006, 17(3): 647-658.[2] M Valera, S A Velastin, Intelligent Distributed Surveillance Systems:a Review, IEEE Vision, Image and Signal Processing, 2005, 152(2): 192-204.[3] J Hsieh, Y Hsua, Boosted string representation and its application to video surveillance, Pattern Recognition 2008,41(10): 3078-3091.[4] I Junejo, H Foroosh, Euclidean path modeling for video surveillance, Image and Vision computing, 2008, 26(4): 512-528.[5] S. Rosiek, F.J. 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