图像处理(中英文)
图像处理中值滤波器中英文对照外文翻译文献
中英文资料对照外文翻译一、英文原文A NEW CONTENT BASED MEDIAN FILTERABSTRACTIn this paper the hardware implementation of a contentbased median filter suitabl e for real-time impulse noise suppression is presented. The function of the proposed ci rcuitry is adaptive; it detects the existence of impulse noise in an image neighborhood and applies the median filter operator only when necessary. In this way, the blurring o f the imagein process is avoided and the integrity of edge and detail information is pre served. The proposed digital hardware structure is capable of processing gray-scale im ages of 8-bit resolution and is fully pipelined, whereas parallel processing is used to m inimize computational time. The architecturepresented was implemented in FPGA an d it can be used in industrial imaging applications, where fast processing is of the utm ost importance. The typical system clock frequency is 55 MHz.1. INTRODUCTIONTwo applications of great importance in the area of image processing are noise filtering and image enhancement [1].These tasks are an essential part of any image pro cessor,whether the final image is utilized for visual interpretation or for automatic an alysis. The aim of noise filtering is to eliminate noise and its effects on the original im age, while corrupting the image as little as possible. To this end, nonlinear techniques (like the median and, in general, order statistics filters) have been found to provide mo re satisfactory results in comparison to linear methods. Impulse noise exists in many p ractical applications and can be generated by various sources, including a number of man made phenomena, such as unprotected switches, industrial machines and car ign ition systems. Images are often corrupted by impulse noise due to a noisy sensor or ch annel transmission errors. The most common method used for impulse noise suppressi on n forgray-scale and color images is the median filter (MF) [2].The basic drawback o f the application of the MF is the blurringof the image in process. In the general case,t he filter is applied uniformly across an image, modifying pixels that arenot contamina ted by noise. In this way, the effective elimination of impulse noise is often at the exp ense of an overalldegradation of the image and blurred or distorted features[3].In this paper an intelligent hardware structure of a content based median filter (CBMF) suita ble for impulse noise suppression is presented. The function of the proposed circuit is to detect the existence of noise in the image window and apply the corresponding MFonly when necessary. The noise detection procedure is based on the content of the im age and computes the differences between the central pixel and thesurrounding pixels of a neighborhood. The main advantage of this adaptive approach is that image blurrin g is avoided and the integrity of edge and detail information are preserved[4,5]. The pro posed digital hardware structure is capable of processing gray-scale images of 8-bitres olution and performs both positive and negative impulse noise removal. The architectt ure chosen is based on a sequence of four basic functional pipelined stages, and parall el processing is used within each stage. A moving window of a 3×3 and 5×5-pixel im age neighborhood can be selected. However, the system can be easily expanded to acc ommodate windows of larger sizes. The proposed structure was implemented using fi eld programmable gate arrays (FPGA). The digital circuit was designed, compiled and successfully simulated using the MAX+PLUS II Programmable Logic Development S ystem by Altera Corporation. The EPF10K200SFC484-1 FPGA device of the FLEX1 0KE device family was utilized for the realization of the system. The typical clock fre quency is 55 MHz and the system can be used for real-time imaging applications whe re fast processing is required [6]. As an example,the time required to perform filtering of a gray-scale image of 260×244 pixels is approximately 10.6 msec.2. ADAPTIVE FILTERING PROCEDUREThe output of a median filter at a point x of an image f depends on the values of t he image points in the neighborhood of x. This neighborhood is determined by a wind ow W that is located at point x of f including n points x1, x2, …, xn of f, with n=2k+1. The proposed adaptive content based median filter can be utilized for impulse noisesu p pression in gray-scale images. A block diagram of the adaptive filtering procedure is depicted in Fig. 1. The noise detection procedure for both positive and negative noise is as follows:(i) We consider a neighborhood window W that is located at point x of the image f. Th e differences between the central pixel at point x and the pixel values of the n-1surr ounding points of the neighborhood (excluding thevalue of the central pixel) are co mputed.(ii) The sum of the absolute values of these differences is computed, denoted as fabs(x ). This value provides ameasure of closeness between the central pixel and its su rrounding pixels.(iii) The value fabs(x) is compared to fthreshold(x), which is anappropriately selected positive integer threshold value and can be modified. The central pixel is conside red to be noise when the value fabs(x) is greater than thethreshold value fthresho d(x).(iv) When the central pixel is considered to be noise it is substituted by the median val ue of the image neighborhood,denoted as fk+1, which is the normal operationof the median filter. In the opposite case, the value of the central pixel is not altered and the procedure is repeated for the next neighborhood window.From the noised etection scheme described, it should be mentioned that the noise detection level procedure can be controlled and a range of pixel values (and not only the fixedvalues of 0 and 255, salt and pepper noise) is considered asimpulse noise.In Fig. 2 the results of the application of the median filter and the CBMF in the gray-sca le image “Peppers” are depicted.More specifically, in Fig. 2(a) the original,uncor rupted image“Peppers” is depicted. In Fig. 2(b) the original imagedegraded by 5% both positive and negative impulse noise isillustrated. In Figs 2(c) and 2(d) the resultant images of the application of median filter and CBMF for a 3×3-pixel win dow are shown, respectively. Finally, the resultant images of the application of m edian filter and CBMF for a 5×5-pixelwindow are presented in Figs 2(e) and 2(f). It can be noticed that the application of the CBMF preserves much better edges a nddetails of the images, in comparison to the median filter.A number of different objective measures can be utilized forthe evaluation of these results. The most wi dely used measures are the Mean Square Error (MSE) and the Normalized Mean Square Error (NMSE) [1]. The results of the estimation of these measures for the two filters are depicted in Table I.For the estimation of these measures, the result ant images of the filters are compared to the original, uncorrupted image.From T able I it can be noticed that the MSE and NMSE estimatedfor the application of t he CBMF are considerably smaller than those estimated for the median filter, in all the cases.Table I. Similarity measures.3. HARDWARE ARCHITECTUREThe structure of the adaptive filter comprises four basic functional units, the mo ving window unit , the median computation unit , the arithmetic operations unit , and th e output selection unit . The input data of the system are the gray-scale values of the pi xels of the image neighborhood and the noise threshold value. For the computation of the filter output a3×3 or 5×5-pixel image neighborhood can be selected. Image input d ata is serially imported into the first stage. In this way,the total number of the inputpin s are 24 (21 inputs for the input data and 3 inputs for the clock and the control signalsr equired). The output data of the system are the resultant gray-scale values computed f or the operation selected (8pins).The moving window unit is the internal memory of the system,used for storing th e input values of the pixels and for realizing the moving window operation. The pixel values of the input image, denoted as “IMAGE_INPUT[7..0]”, areimported into this u nit in serial. For the representation of thethreshold value used for the detection of a no Filter Impulse noise 5% mse Nmse(×10-2) 3×3 5×5 3×3 5×5Median CBMF 57.554 35.287 130.496 84.788 0.317 0.194 0.718 0.467ise pixel 13 bits are required. For the moving window operation a 3×3 (5×5)-pixel sep entine type memory is used, consisting of 9 (25)registers. In this way,when the windoP1 P2 P3w is moved into the next image neighborhood only 3 or 5 pixel values stored in the memory are altered. The “en5×5” control signal is used for the selection of the size of th e image window, when“en5×5” is equal to “0” (“1”) a 3×3 (5×5)-pixel neighborhood is selected. It should be mentioned that the modules of the circuit used for the 3×3-pix el window are utilized for the 5×5-pixel window as well. For these modules, 2-to-1mu ltiplexers are utilized to select the appropriate pixel values,where necessary. The mod ules that are utilized only in the case of the 5×5-pixel neighborhood are enabled by th e“en5×5” control signal. The outputs of this unit are rows ofpixel values (3 or 5, respe ctively), which are the inputs to the median computation unit.The task of the median c omputation unit is to compute themedian value of the image neighborhood in order to substitutethe central pixel value, if necessary. For this purpose a25-input sorter is utili zeed. The structure of the sorter has been proposed by Batcher and is based on the use of CS blocks. ACS block is a max/min module; its first output is the maximumof the i nputs and its second output the minimum. The implementation of a CS block includes a comparator and two 2-to-1 multiplexers. The outputs values of the sorter, denoted a s “OUT_0[7..0]”…. “OUT_24[7..0]”, produce a “sorted list” of the 25 initial pixel val ues. A 2-to-1 multiplexer isused for the selection of the median value for a 3×3 or 5×5-pixel neighborhood.The function of the arithmetic operations unit is to computethe value fabs(x), whi ch is compared to the noise threshold value in the final stage of the adaptive filter.The in puts of this unit are the surrounding pixel values and the central pixelof the neighb orhood. For the implementation of the mathematical expression of fabs(x), the circuit of this unit contains a number of adder modules. Note that registers have been used to achieve a pipelined operation. An additional 2-to-1 multiplexer is utilized for the selec tion of the appropriate output value, depending on the “en5×5” control signal. From th e implementation point of view, the use of arithmetic blocks makes this stage hardwar e demanding.The output selection unit is used for the selection of the appropriateoutput value of the performed noise suppression operation. For this selection, the corresponding no ise threshold value calculated for the image neighborhood,“NOISE_THRES HOLD[1 2..0]”,is employed. This value is compared to fabs(x) and the result of the comparison Classifies the central pixel either as impulse noise or not. If thevalue fabs(x) is greater than the threshold value fthreshold(x) the central pixel is positive or negative impulse noise and has to be eliminated. For this reason, the output of the comparison is used as the selection signal of a 2-to-1 multiplexer whose inputs are the central pixel and the c orresponding median value for the image neighborhood. The output of the multiplexer is the output of this stage and the final output of the circuit of the adaptive filter.The st ructure of the CBMF, the computation procedure and the design of the four aforeme n tioned units are illustrated in Fig. 3.ImagewindoeFigure 1: Block diagram of the filtering methodFigure 2: Results of the application of the CBMF: (a) Original image, (b) noise corrupted image (c) Restored image by a 3x3 MF, (d) Restored image by a 3x3 CBMF, (e) Restored image by a 5x5 MF and (f) Restored image by a 5x5 CBMF.4. IMPLEMENTATION ISSUESThe proposed structure was implemented in FPGA,which offer an attractive com bination of low cost, high performance and apparent flexibility, using the software pa ckage+PLUS II of Altera Corporation. The FPGA used is the EPF10K200SFC484-1 d evice of the FLEX10KE device family,a device family suitable for designs that requir e high densities and high I/O count. The 99% of the logic cells(9965/9984 logic cells) of the device was utilized to implement the circuit . The typical operating clock frequ ency of the system is 55 MHz. As a comparison, the time required to perform filtering of a gray-scale image of 260×244 pixelsusing Matlab® software on a Pentium 4/2.4 G Hz computer system is approximately 7.2 sec, whereas the corresponding time using h ardware is approximately 10.6 msec.The modification of the system to accommodate windows oflarger sizes can be done in a straightforward way, requiring onlya small nu mber of changes. More specifically, in the first unit the size of the serpentine memory P4P5P6P7P8P9SubtractorarryMedianfilteradder comparatormuitiplexerf abc(x)valueand the corresponding number of multiplexers increase following a square law. In the second unit, the sorter module should be modified,and in the third unit the number of the adder devicesincreases following a square law. In the last unit no changes are requ ired.5. CONCLUSIONSThis paper presents a new hardware structure of a content based median filter, ca pable of performing adaptive impulse noise removal for gray-scale images. The noise detection procedure takes into account the differences between the central pixel and th e surrounding pixels of a neighborhood.The proposed digital circuit is capable ofproce ssing grayscale images of 8-bit resolution, with 3×3 or 5×5-pixel neighborhoods as op tions for the computation of the filter output. However, the design of the circuit is dire ctly expandableto accommodate larger size image windows. The adaptive filter was d eigned and implemented in FPGA. The typical clock frequency is 55 MHz and the sys tem is suitable forreal-time imaging applications.REFERENCES[1] W. K. Pratt, Digital Image Processing. New York: Wiley,1991.[2] G. R. Arce, N. C. Gallagher and T. Nodes, “Median filters:Theory and applicat ions,” in Advances in ComputerVision and Image Processing, Greenwich, CT: JAI, 1986.[3] T. A. Nodes and N. C. Gallagher, Jr., “The output distributionof median type filte rs,” IEEE Transactions onCommunications, vol. COM-32, pp. 532-541, May1984.[4] T. Sun and Y. Neuvo, “Detail-preserving median basedfilters in imageprocessing,” Pattern Recognition Letters,vol. 15, pp. 341-347, Apr. 1994.[5] E. Abreau, M. Lightstone, S. K. Mitra, and K. Arakawa,“A new efficient approachfor the removal of impulsenoise from highly corrupted images,” IEEE Transa ctionson Image Processing, vol. 5, pp. 1012-1025, June 1996.[6] E. R. Dougherty and P. Laplante, Introduction to Real-Time Imaging, Bellingham:SPIE/IEEE Press, 1995.二、英文翻译基于中值滤波的新的内容摘要在本设计中的提出了基于中值滤波的硬件实现用来抑制脉冲噪声的干扰。
图像处理单词
图像处理核心单词Photoreceptor cells:感光细胞Rod: 杆状细胞Cone: 锥状细胞Retina: 视网膜Iris: 虹膜Fovea: 中央凹Visual cortex: 视觉皮层CCD: charge-coupled devices电荷耦合器件Scanning: 扫描Continuous: 连续的Discrete: 离散的Digitization: 数字化Sampling: 采样Quantization: 量化Band-limited function: 带宽有限函数ADC: analog-to-digital converter 模数转换器Pixel: picture element 象素Gray-scale :灰度Gray level:灰度级Gray-scale resolution: 灰度分辨率Resolution: 分辨率Sample density: 采样密度Bit: 比特Byte: 字节Pixel spacing: 象素间距Contrast: 对比度Noise: 噪声SNR: signal-to-noise ratio 信噪比Frame: 帧Field: 场Line: 行，线Interlaced scanning: 隔行扫描Frame grabber: 帧抓取器Image enhancement:图象增强Image quality:图象质量Algorithm: 算法Globe operation: 全局运算Local operation: 局部运算Point operation: 点运算Spatial: 空间的Spatial domain:空间域Spatial coordinate:空间坐标Linear: 线性Nonlinear: 非线性Frequency: 频率Frequency variable: 频率变量Frequency domain: 频域Fourier transform: 傅立叶变换One-dimensional Fourier transform: 一维傅立叶变换Two-dimensional Fourier transform: 二维傅立叶变换Discrete Fourier transform(DFT): 离散傅立叶变换Fast Fourier transform(FFT): 快速傅立叶变换Inverse Fourier transform: 傅立叶反变换Contrast enhancement: 对比度增强Contrast stretching: 对比度扩展Gray-scale transformation(GST): 灰度变换Logarithm transformation: 对数变换Exponential transformation: 指数变换Threshold: 阈值Thresholding: 二值化、门限化False contour: 假轮廓Histogram: 直方图Multivariable histogram: 多变量直方图Histogram modification: 直方图调整、直方图修改Histogram equalization: 直方图均衡化Histogram specification: 直方图规定化Histogram matching: 直方图匹配Histogram thresholing: 直方图门限化Probability density function(PDF): 概率密度函数Cumulative distribution function(CDF): 累积分布函数Slope: 斜率Normalized: 归一化Inverse function: 反函数Calculus: 微积分Derivative: 导数Integral: 积分Monotonic function: 单调函数Infinite: 无穷大Infinitesimal: 无穷小Equation: 方程Numerator: 分子Denominator: 分母Coefficient: 系数Image smoothing: 图象平滑Image averaging: 图象平均Expectation: 数学期望Mean: 均值Variance: 方差Median filtering: 中值滤波Neighborhood: 邻域Filter: 滤波器Lowpass filter: 低通滤波器Highpass filter: 高通滤波器Bandpass filter: 带通滤波器Bandreject filter、Bandstop filter: 带阻滤波器Ideal filter: 理想滤波器Butterworth filter: 巴特沃思滤波器Exponential filter: 指数滤波器Trapezoidal filter: 梯形滤波器Transfer function: 传递函数Frequency response: 频率响应Cut-off frequency: 截止频率Spectrum: 频谱Amplitude spectrum: 幅值谱Phase spectrum: 相位谱Power spectrum: 功率谱Blur: 模糊Random: 随机Additive: 加性的Uncorrelated: 互不相关的Salt & pepper noise: 椒盐噪声Gaussian noise: 高斯噪声Speckle noise: 斑点噪声Grain noise: 颗粒噪声Bartlett window: 巴特雷窗Hamming window: 汉明窗Hanning window: 汉宁窗Blackman window: 布赖克曼窗Convolution: 卷积Convolution kernel: 卷积核。
图像处理外文翻译 (2)
附录一英文原文Illustrator software and Photoshop software difference Photoshop and Illustrator is by Adobe product of our company, but as everyone more familiar Photoshop software, set scanning images, editing modification, image production, advertising creative, image input and output in one of the image processing software, favored by the vast number of graphic design personnel and computer art lovers alike.Photoshop expertise in image processing, and not graphics creation. Its application field, also very extensive, images, graphics, text, video, publishing various aspects have involved. Look from the function, Photoshop can be divided into image editing, image synthesis, school tonal color and special effects production parts. Image editing is image processing based on the image, can do all kinds of transform such as amplifier, reducing, rotation, lean, mirror, clairvoyant, etc. Also can copy, remove stain, repair damaged image, to modify etc. This in wedding photography, portrait processing production is very useful, and remove the part of the portrait, not satisfied with beautification processing, get let a person very satisfactory results.Image synthesis is will a few image through layer operation, tools application of intact, transmit definite synthesis of meaning images, which is a sure way of fine arts design. Photoshop provide drawing tools let foreign image and creative good fusion, the synthesis of possible make the image is perfect.School colour in photoshop with power is one of the functions of deep, the image can be quickly on the color rendition, color slants adjustment and correction, also can be in different colors to switch to meet in different areas such as web image design, printing and multimedia application.Special effects production in photoshop mainly by filter, passage of comprehensive application tools and finish. Including image effects of creative and special effects words such as paintings, making relief, gypsum paintings, drawings, etc commonly used traditional arts skills can be completed by photoshop effects. And all sorts of effects of production aremany words of fine arts designers keen on photoshop reason to study.Users in the use of Photoshop color function, will meet several different color mode: RGB, CMY K, HSB and Lab. RGB and CMYK color mode will let users always remember natural color, users of color and monitors on the printed page color is a totally different approach to create. The monitor is by sending red, green, blue three beams to create color: it is using RGB (red/green/blue) color mode. In order to make a complex color photographs on a continuous colour and lustre effect, printing technology used a cyan, the red, yellow and black ink presentation combinations from and things, reflect or absorb all kinds of light wavelengths. Through overprint) this print (add four color and create color is CMYK (green/magenta/yellow/black) yan color part of a pattern. HSB (colour and lustre/saturation/brightness) color model is based on the way human feelings, so the color will be natural color for customer computer translation of the color create provides an intuitive methods. The Lab color mode provides a create "don't rely on equipment" color method, this also is, no matter use what monitors.Photoshop expertise in image processing, and not graphics creation. It is necessary to distinguish between the two concepts. Image processing of the existing bitmap image processing and use edit some special effects, the key lies in the image processing processing; Graphic creation software is according to their own idea originality, using vector graphics to design graphics, this kind of software main have another famous company Adobe Illustrator and Macromedia company software Freehand.As the world's most famous Adobe Illustrator, feat graphics software is created, not graphic image processing. Adobe Illustrator is published, multimedia and online image industry standard vector illustration software. Whether production printing line draft of the designers and professional Illustrator, production multimedia image of artists, or Internet page or online content producers Illustrator, will find is not only an art products tools. This software for your line of draft to provide unprecedented precision and control, is suitable for the production of any small design to large complex projects.Adobe Illustrator with its powerful function and considerate user interface has occupied most of the global vector editing software share. With incomplete statistics global 37% of stylist is in use Adobe Illustrator art design. Especially the patent PostScript Adobe companybased on the use of technology, has been fully occupied professional Illustrator printed fields. Whether you're line art designers and professional Illustrator, production multimedia image of artists, or Internet page or online content producers, had used after Illustrator, its formidable will find the function and concise interface design style only Freehand to compare. (Macromedia Freehand is launched vector graphics software company, following the Macromedia company after the merger by Adobe Illustrator and will decide to continue the development of the software have been withdrawn from market).Adobe company in 1987 when they launched the Illustrator1.1 version. In the following year, and well platform launched 2.0 version. Illustrator really started in 1988, should say is introduced on the Mac Illustrator 88 version. A year after the upgrade to on the Mac version3.0 in 1991, and spread to Unix platforms. First appeared on the platform in the PC version4.0 version of 1992, this version is also the earliest Japanese transplant version. And in the MAC is used most is5.0/5.5 version, because this version used Dan Clark's do alias (anti-aliasing display) display engine is serrated, make originally had been in graphic display of vector graphics have a qualitative leap. At the same time on the screen making significant reform, style and Photoshop is very similar, so for the Adobe old users fairly easy to use, it is no wonder that did not last long, and soon also popular publishing industry launched Japanese. But not offering PC version. Adobe company immediately Mac and Unix platforms in launched version6.0. And by Illustrator real PC users know is introduced in 1997, while7.0 version of Mac and Windows platforms launch. Because the 7.0 version USES the complete PostScript page description language, make the page text and graphics quality got again leap. The more with her and Photoshop good interchangeability, won a good reputation. The only pity is the support of Chinese 7.0 abysmal. In 1998 the company launched landmark Adobe Illustrator8.0, making version - Illustrator became very perfect drawing software, is relying on powerful strength, Adobe company completely solved of Chinese characters and Japanese language support such double byte, more increased powerful "grid transition" tool (there are corresponding Draw9.0 Corel, but the effect the function of poor), text editing tools etc function, causes its fully occupy the professional vector graphics software's supremacy.Adobe Illustrator biggest characteristics is the use of beisaier curve, make simpleoperation powerful vector graphics possible. Now it has integrated functions such as word processing, coloring, not only in illustrations production, in printing products (such as advertising leaflet, booklet) design manufacture aspect is also widely used, in fact has become desktop publishing or (DTP) industry default standard. Its main competitors are in 2005, but MacromediaFreehand Macromedia had been Adobe company mergers.So-called beisaier curve method, in this software is through "the pen tool" set "anchor point" and "direction line" to realize. The average user in the beginning when use all feel not accustomed to, and requires some practice, but once the master later can follow one's inclinations map out all sorts of line, and intuitive and reliable.It also as Creative Suite of software suit with important constituent, and brother software - bitmap graphics software Photoshop have similar interface, and can share some plug-ins and function, realize seamless connection. At the same time it also can put the files output for Flash format. Therefore, can pass Illustrator let Adobe products and Flash connection.Adobe Illustrator CS5 on May 17, 2010 issue. New Adobe Illustrator CS5 software can realize accurate in perspective drawing, create width variable stroke, use lifelike, make full use of paint brush with new Adobe CS Live online service integration. AI CS5 has full control of the width zoom along path variable, and stroke, arrows, dashing and artistic brushes. Without access to multiple tools and panel, can directly on the sketchpad merger, editing and filling shape. AI CS5 can handle a file of most 100 different size, and according to your sketchpad will organize and check them.Here in Adobe Illustrator CS5, for example, briefly introduce the basic function: Adobe IllustratorQuick background layerWhen using Illustrator after making good design, stored in Photoshop opens, if often pattern is in a transparent layer, and have no background ground floor. Want to produce background bottom, are generally add a layer, and then executed merge down or flatten, with background ground floor. We are now introducing you a quick method: as long as in diagram level on press the upper right version, choose new layer, the arrow in the model selection and bottom ", "background can quickly produce. However, in Photoshop 5 after the movementmerged into one instruction, select menu on the "new layer is incomplete incomplete background bottom" to finish.Remove overmuch type clothWhen you open the file, version 5 will introduce the Illustrator before Illustrator version created files disused zone not need. In order to remove these don't need in the zone, click on All Swatches palette Swatches icon and then Select the Select clause in the popup menu, and Trash Unused. Click on the icon to remove irrelevant type cloth. Sometimes you must repeat selection and delete processes to ensure that clear palette. Note that complex documents will take a relatively long time doing cleanup.Put the fabric to define the general-screeningIn Illustrator5 secondary color and process color has two distinct advantages compared to establish for easy: they provide HuaGan tonal; And when you edit the general-screening prescription, be filled some of special color objects will be automatically updated into to the new color. Because process color won't let you build tonal and provides automatic updates, you may want to put all the fabric is defined as the general-screening. But to confirm Illustrator, when you are in QuarkXPress or when PageMaker quaclrochramatic must keep their into process of color.Preferred using CMYKBecause of Illustrator7 can let you to CMYK, RGB and HSB (hue, saturation, bright) color mode, so you want to establish color the creation of carefully, you can now contains the draft with the combination of these modes created objects. When you do, they may have output various kinds of unexpected things will happen. Printing output file should use CMYK; Only if you don't use screen display manuscript RGB. If your creation draft will also be used for printing and screen display, firstly with CMYK create printing output file, then use to copy it brings As ordered the copy and modify to the appropriate color mode.Information source：" Baidu encyclopedia "附录二中文译文Illustrator软件与Photoshop软件的区别Photoshop与Illustrator都是由Adobe公司出品的，而作为大家都比较熟悉的Photoshop软件，集图像扫描、编辑修改、图像制作、广告创意，图像输入与输出于一体的图形图像处理软件，深受广大平面设计人员和电脑美术爱好者的喜爱。
图像处理-毕设论文外文翻译(翻译+原文)
英文资料翻译Image processing is not a one step process．We are able to distinguish between several steps which must be performed one after the other until we can extract the data of interest from the observed scene．In this way a hierarchical processing scheme is built up as sketched in Fig．The figure gives an overview of the different phases of image processing.Image processing begins with the capture of an image with a suitable，not necessarily optical，acquisition system．In a technical or scientific application，we may choose to select an appropriate imaging system．Furthermore，we can set up the illumination system，choose the best wavelength range，and select other options to capture the object feature of interest in the best way in an image．Once the image is sensed，it must be brought into a form that can be treated with digital computers.This process is called digitization.With the problems of traffic are more and more serious. Thus Intelligent Transport System (ITS) comes out. The subject of the automatic recognition of license plate is one of the most significant subjects that are improved from the connection of computer vision and pattern recognition. The image imputed to the computer is disposed and analyzed in order to localization the position and recognition the characters on the license plate express these characters in text string form The license plate recognition system (LPSR) has important application in ITS. In LPSR, the first step is for locating the license plate in the captured image which is very important for character recognition. The recognition correction rate of license plate is governed by accurate degree of license plate location. In this paper, several of methods in image manipulation are compared and analyzed, then come out the resolutions for localization of the car plate. The experiences show that the good result has been got with these methods. The methods based on edge map and frequency analysis is used in the process of the localization of the license plate, that is to say, extracting the characteristics of the license plate in the car images after being checked up forthe edge, and then analyzing and processing until the probably area of license plate is extracted.The automated license plate location is a part of the image processing ,it’s also an important part in the intelligent traffic system．It is the key step in the Vehicle License Plate Recognition(LPR).A method for the recognition of images of different backgrounds and different illuminations is proposed in the paper.the upper and lower borders are determined through the gray variation regulation of the character distribution.The left and right borders are determined through the black-white variation of the pixels in every row.The first steps of digital processing may include a number of different operations and are known as image processing．If the sensor has nonlinear characteristics, these need to be corrected．Likewise，brightness and contrast of the image may require improvement．Commonly，too，coordinate transformations are needed to restore geometrical distortions introduced during image formation．Radiometric and geometric corrections are elementary pixel processing operations．It may be necessary to correct known disturbances in the image，for instance caused by a defocused optics，motion blur，errors in the sensor，or errors in the transmission of image signals．We also deal with reconstruction techniques which are required with many indirect imaging techniques such as tomography that deliver no direct image.A whole chain of processing steps is necessary to analyze and identify objects．First，adequate filtering procedures must be applied in order to distinguish the objects of interest from other objects and the background．Essentially，from an image（or several images），one or more feature images are extracted．The basic tools for this task are averaging and edge detection and the analysis of simple neighborhoods and complex patterns known as texture in image processing．An important feature of an object is also its motion．Techniques to detect and determine motion are necessary．Then the object has to be separated from the background．This means that regions of constant features and discontinuities must be identified．This process leads to alabel image．Now that we know the exact geometrical shape of the object，we can extract further information such as the mean gray value，the area，perimeter，and other parameters for the form of the object[3]．These parameters can be used to classify objects．This is an important step in many applications of image processing，as the following examples show：In a satellite image showing an agricultural area，we would like to distinguish fields with different fruits and obtain parameters to estimate their ripeness or to detect damage by parasites．There are many medical applications where the essential problem is to detect pathologi-al changes．A classic example is the analysis of aberrations in chromosomes．Character recognition in printed and handwritten text is another example which has been studied since image processing began and still poses significant difficulties．You hopefully do more，namely try to understand the meaning of what you are reading．This is also the final step of image processing，where one aims to understand the observed scene．We perform this task more or less unconsciously whenever we use our visual system．We recognize people，we can easily distinguish between the image of a scientific lab and that of a living room，and we watch the traffic to cross a street safely．We all do this without knowing how the visual system works．For some times now，image processing and computer-graphics have been treated as two different areas．Knowledge in both areas has increased considerably and more complex problems can now be treated．Computer graphics is striving to achieve photorealistic computer-generated images of three-dimensional scenes，while image processing is trying to reconstruct one from an image actually taken with a camera．In this sense，image processing performs the inverse procedure to that of computer graphics．We start with knowledge of the shape and features of an object—at the bottom of Fig. and work upwards until we get a two-dimensional image．To handle image processing or computer graphics，we basically have to work from the same knowledge．We need to know the interaction between illumination and objects，how a three-dimensional scene is projected onto an image plane，etc．There are still quite a few differences between an image processing and a graphics workstation．But we can envisage that，when the similarities and interrelations between computergraphics and image processing are better understood and the proper hardware is developed，we will see some kind of general-purpose workstation in the future which can handle computer graphics as well as image processing tasks[5]．The advent of multimedia，i. e. ，the integration of text，images，sound，and movies，will further accelerate the unification of computer graphics and image processing.In January 1980 Scientific American published a remarkable image called Plume2，the second of eight volcanic eruptions detected on the Jovian moon by the spacecraft Voyager 1 on 5 March 1979．The picture was a landmark image in interplanetary exploration—the first time an erupting volcano had been seen in space．It was also a triumph for image processing．Satellite imagery and images from interplanetary explorers have until fairly recently been the major users of image processing techniques，where a computer image is numerically manipulated to produce some desired effect-such as making a particular aspect or feature in the image more visible.Image processing has its roots in photo reconnaissance in the Second World War where processing operations were optical and interpretation operations were performed by humans who undertook such tasks as quantifying the effect of bombing raids．With the advent of satellite imagery in the late 1960s，much computer-based work began and the color composite satellite images，sometimes startlingly beautiful, have become part of our visual culture and the perception of our planet．Like computer graphics，it was until recently confined to research laboratories which could afford the expensive image processing computers that could cope with the substantial processing overheads required to process large numbers of high-resolution images．With the advent of cheap powerful computers and image collection devices like digital cameras and scanners，we have seen a migration of image processing techniques into the public domain．Classical image processing techniques are routinely employed bygraphic designers to manipulate photographic and generated imagery，either to correct defects，change color and so on or creatively to transform the entire look of an image by subjecting it to some operation such as edge enhancement.A recent mainstream application of image processing is the compression of images—either for transmission across the Internet or the compression of moving video images in video telephony and video conferencing．Video telephony is one of the current crossover areas that employ both computer graphics and classical image processing techniques to try to achieve very high compression rates．All this is part of an inexorable trend towards the digital representation of images．Indeed that most powerful image form of the twentieth century—the TV image—is also about to be taken into the digital domain．Image processing is characterized by a large number of algorithms that are specific solutions to specific problems．Some are mathematical or context-independent operations that are applied to each and every pixel．For example，we can use Fourier transforms to perform image filtering operations．Others are“algorithmic”—we may use a complicated recursive strategy to find those pixels that constitute the edges in an image．Image processing operations often form part of a computer vision system．The input image may be filtered to highlight or reveal edges prior to a shape detection usually known as low-level operations．In computer graphics filtering operations are used extensively to avoid abasing or sampling artifacts．中文翻译图像处理不是一步就能完成的过程。
matlab图像处理中英文翻译文献培训课件
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 twodistinct stages: training offline and matching online.During the training stage, robot collects the images of the environment where it works and processes the images to extract global features that represent the scene. Some approaches were used to analyze the data-set of image directly and some primary features were found, such as the PCA method [3]. However, the PCA method is not effective in distinguishing the classes of features. Another type of approach uses appearance features including color, texture and edge density to represent the image. For example, ZHOU et al[4] used multidimensional histograms to describe global appearance features. This method is simple but sensitive to scale and illumination changes. In fact, all kinds of global image features are suffered from the change of environment.LOWE [5] presented a SIFT method that uses similarity invariant descriptors formed by characteristic scale and orientation at interest points to obtain the features. The features are invariant to image scaling, translation, rotation and partially invariant to illumination changes. But SIFT may generate 1 000 or more interest points, which may slow down the processor dramatically.During the matching stage, nearest neighbor strategy(NN) is widely adopted for its facility and intelligibility[6]. But it cannot capture the contribution of individual feature for scene recognition. In experiments, the NN is not good enough to express the similarity between two patterns. Furthermore, the selected features can not represent the scene thoroughly according to the state-of-art pattern recognition, which makes recognition not reliable[7].So in this work a new recognition system is presented, which is more reliable and effective if it is used in a complex mine environment. In this system, we improve the invariance by extracting salient local image regions as landmarks to replace the whole image to deal with large changes in scale, 2D rotation and viewpoint. And the number of interest points is reduced effectively, which makes the processing easier. Fuzzy recognition strategy is designed to recognize the landmarks in place of NN, which can strengthen the contribution of individual feature for scene recognition. Because of its partial information resuming ability, hidden Markov model is adopted to organize those landmarks, which can capture the structure or relationship among them. So scene recognition can be transformed to the evaluation problem of HMM, which makes recognition robust.2 Salient local image regions detectionResearches on biological vision system indicate that organism (like drosophila) often pays attention to certain special regions in the scene for their behavioral relevance or local image cues while observing surroundings [8]. These regions can be taken as natural landmarks to effectively represent and distinguish different environments. Inspired by those, we use center-surround difference method to detect salient regions in multi-scale image spaces. The opponencies of color and texture are computed to create the saliency map.Follow-up, sub-image centered at the salient position in S is taken as the landmark region. The size of the landmark region can be decided adaptively according to the changes of gradient orientation of the local image [11].Mobile robot navigation requires that natural landmarks should be detected stably when environments change to some extent. To validate the repeatability on landmark detection of our approach, we have done some experiments on the cases of scale, 2D rotation and viewpoint changes etc. Fig.1 shows that the door is detected for its saliency when viewpoint changes. More detailed analysis and results about scale and rotation can be found in our previous works[12].3 Scene recognition and localizationDifferent from other scene recognition systems, our system doesn’t need training offline. In other words, our scenes are not classified in advance. When robot wanders, scenes captured at intervals of fixed time are used to build the vertex of a topological map, which represents the place where robot locates. Although the map’s geometric layout is ignored by the localization system, it is useful for visualization and debugging[13] and beneficial to path planning. So localization means searching the best match of current scene on the map. In this paper hidden Markov model is used to organize the extracted landmarks from current scene and create the vertex of topological map for its partial information resuming ability.Resembled by panoramic vision system, robot looks around to get omni-images. FromFig.1 Experiment on viewpoint changeseach image, salient local regions are detected and formed to be a sequence, named as landmark sequence whose order is the same as the image sequence. Then a hidden Markov model is created based on the landmark sequence involving k salient local image regions, which is taken as the description of the place where the robot locates. In our system EVI-D70 camera has a view field of ±170°. Considering the overlap effect, we sample environment every 45° to get 8 images.Let the 8 images as hidden state Si (1≤i≤8), the created HMM can be illustrated by Fig.2. The parameters of HMM, aij and bjk, are achieved by learning, using Baulm-Welch algorithm[14]. The threshold of convergence is set as 0.001.As for the edge of topological map, we assign it with distance information between twovertices. The distances can be computed according to odometry readings.Fig.2 HMM of environmentTo locate itself on the topological map, robot must run its ‘eye’ on environment and extract a landmark sequence L1′ −Lk′ , then search the map for the best matched vertex (scene). Different from traditional probabilistic localization[15], in our system localization problem can be converted to the evaluation problem of HMM. The vertex with the greatest evaluation value, which must also be greater than a threshold, is taken as the best matched vertex, which indicates the most possible place where the robot is.4 Match strategy based on fuzzy logicOne of the key issues in image match problem is to choose the most effective features or descriptors to represent the original image. Due to robot movement, those extracted landmark regions will change at pixel level. So, the descriptors or features chosen should be invariant to some extent according to the changes of scale, rotation and viewpoint etc. In this paper, we use 4 features commonly adopted in the community that are briefly described as follows.GO: Gradient orientation. It has been proved that illumination and rotation changes are likely to have less influence on it[5].ASM and ENT: Angular second moment and entropy, which are two texture descriptors.H: Hue, which is used to describe the fundamental information of the image.Another key issue in match problem is to choose a good match strategy or algorithm. Usually nearest neighbor strategy (NN) is used to measure the similarity between two patterns. But we have found in the experiments that NN can’t adequately exhibit the individual descriptor or feature’s contribution to similarity measurement. As indicated in Fig.4, the input image Fig.4(a) comes from different view of Fig.4(b). But the distance between Figs.4(a) and (b) computed by Jefferey divergence is larger than Fig.4(c).To solve the problem, we design a new match algorithm based on fuzzy logic for exhibiting the subtle changes of each features. The algorithm is described as below.And the landmark in the database whose fused similarity degree is higher than any others is taken as the best match. The match results of Figs.2(b) and (c) are demonstrated by Fig.3. As indicated, this method can measure the similarity effectively between two patterns.Fig.3 Similarity computed using fuzzy strategy5 Experiments and analysisThe localization system has been implemented on a mobile robot, which is built by our laboratory. The vision system is composed of a CCD camera and a frame-grabber IVC-4200. The resolution of image is set to be 400×320 and the sample frequency is set to be 10 frames/s. The computer system is composed of 1 GHz processor and 512 M memory, which is carried by the robot. Presently the robot works in indoor environments.Because HMM is adopted to represent and recognize the scene, our system has the ability to capture the discrimination about distribution of salient local image regions and distinguish similar scenes effectively. Table 1 shows the recognition result of static environments including 5 laneways and a silo. 10 scenes are selected from each environment and HMMs are created for each scene. Then 20 scenes are collected when the robot enters each environment subsequently to match the 60 HMMs above.In the table, “truth” m eans that the scene to be localized matches with the right scene (the evaluation value of HMM is 30% greater than the second high evaluation). “Uncertainty” means that the evaluation value of HMM is greater than the second high evaluation under 10%. “Error match” means that the scene to be localized matches with the wrong scene. In the table, the ratio of error match is 0. But it is possible that the scene to be localized can’t match any scenes and new vertexes are created. Furthermore, the “ratio of truth” about silo is lower because salient cues arefewer in this kind of environment.In the period of automatic exploring, similar scenes can be combined. The process can be summarized as: when localization succeeds, the current landmark sequence is added to the accompanying observation sequence of the matched vertex un-repeatedly according to their orientation (including the angle of the image from which the salient local region and the heading of the robot come). The parameters of HMM are learned again.Compared with the approaches using appearance features of the whole image (Method 2, M2), our system (M1) uses local salient regions to localize and map, which makes it have more tolerance of scale, viewpoint changes caused by robot’s movement and higher ratio of recognition and fewer amount of vertices on the topological map. So, our system has better performance in dynamic environment. These can be seen in Table 2. Laneways 1, 2, 4, 5 are in operation where some miners are working, which puzzle the robot.6 Conclusions1) Salient local image features are extracted to replace the whole image to participate in recognition, which improve the tolerance of changes in scale, 2D rotation and viewpoint of environment image.2) Fuzzy logic is used to recognize the local image, and emphasize the individual feature’s contribution to recognition, which improves the reliability of landmarks.3) HMM is used to capture the structure or relationship of those local images, which converts the scene recognition problem into the evaluation problem of HMM.4) The results from the above experiments demonstrate that the mine rescue robot scene recognition system has higher ratio of recognition and localization.Future work will be focused on using HMM to deal with the uncertainty of localization.附录B 中文翻译基于视觉的矿井救援机器人场景识别CUI Yi-an(崔益安), CAI Zi-xing(蔡自兴), WANG Lu(王璐)摘要：基于模糊逻辑和隐马尔可夫模型（HMM），论文提出了一个新的场景识别系统，可应用于紧急情况下矿山救援机器人的定位。
图像处理英文
GLCM:灰度共生矩阵indexing service：索引服务Binary image二值图像；只有两级灰度的数字图像（通常为0和1，黑和白）Blur 模糊；由于散焦、低通滤波、摄像机运动等引起的图像清晰度的下降。
Shape：形状，shape from texture，纹理形状mathematical morphology: 数学形态学Border 边框；一副图像的首、末行或列。
Boundary chain code 边界链码；定义一个物体边界的方向序列。
Boundary pixel 边界像素；至少和一个背景像素相邻接的内部像素Boundary tracking 边界跟踪；一种图像分割技术，通过沿弧从一个像素顺序探索到下一个像素将弧检测出。
Brightness 亮度；和图像一个点相关的值，表示从该点的物体发射或放射的光的量。
Change detection 变化检测；通过相减等操作将两幅匹准图像的像素加以比较Closed curve 封闭曲线；一条首尾点处于同一位置的曲线。
Cluster 聚类、集群；在空间（如在特征空间）中位置接近的点的集合。
Cluster analysis 聚类分析；在空间中对聚类的检测，度量和描述。
Concave 凹的；物体是凹的是指至少存在两个物体内部的点，其连线不能完全包含在物体内Connected 连通的神经网络：neural networkContour encoding 轮廓编码；对具有均匀灰度的区域，只将其边界进行编码的一种图像压缩技术。
Contrast 对比度；物体平均亮度（或灰度）与其周围背景的差别程度Contrast stretch 对比度扩展；一种线性的灰度变换Convolution 卷积；一种将两个函数组合成第三个函数的运算，卷积刻画了线性移不变系统的运算。
Deblurring 去模糊；1一种降低图像模糊，锐化图像细节的运算。
2 消除或降低图像的模糊，通常是图像复原或重构的一个步骤。
图像处理外文翻译
附录A3 Image Enhancement in the Spatial DomainThe principal objective of enhancement is to process an image so that the result is more suitable than the original image for a specific application. The word specific is important, because it establishes at the outset than the techniques discussed in this chapter are very much problem oriented. Thus, for example, a method that is quite useful for enhancing X-ray images may not necessarily be the best approach for enhancing pictures of Mars transmitted by a space probe. Regardless of the method used .However, image enhancement is one of the most interesting and visually appealing areas of image processing.Image enhancement approaches fall into two broad categories: spatial domain methods and frequency domain methods. The term spatial domain refers to the image plane itself, and approaches in this category are based on direct manipulation of pixels in an image. Fourier transform of an image. Spatial methods are covered in this chapter, and frequency domain enhancement is discussed in Chapter 4.Enhancement techniques based on various combinations of methods from these two categories are not unusual. We note also that many of the fundamental techniques introduced in this chapter in the context of enhancement are used in subsequent chapters for a variety of other image processing applications.There is no general theory of image enhancement. When an image is processed for visual interpretation, the viewer is the ultimate judge of how well a particular method works. Visual evaluation of image quality is a highly is highly subjective process, thus making the definition of a “good image” an elusive standard by which to compare algorithm performance. When the problem is one of processing images for machine perception, the evaluation task is somewhat easier. For example, in dealing with a character recognition application, and leaving aside other issues such as computational requirements, the best image processing method would be the one yielding the best machine recognition results. However, even in situations when aclear-cut criterion of performance can be imposed on the problem, a certain amount of trial and error usually is required before a particular image enhancement approach is selected.3.1 BackgroundAs indicated previously, the term spatial domain refers to the aggregate of pixels composing an image. Spatial domain methods are procedures that operate directly on these pixels. Spatial domain processes will be denotes by the expression()[]=(3.1-1)g x y T f x y,(,)where f(x, y) is the input image, g(x, y) is the processed image, and T is an operator on f, defined over some neighborhood of (x, y). In addition, T can operate on a set of input images, such as performing the pixel-by-pixel sum of K images for noise reduction, as discussed in Section 3.4.2.The principal approach in defining a neighborhood about a point (x, y) is to use a square or rectangular subimage area centered at (x, y).The center of the subimage is moved from pixel to starting, say, at the top left corner. The operator T is applied at each location (x, y) to yield the output, g, at that location. The process utilizes only the pixels in the area of the image spanned by the neighborhood. Although other neighborhood shapes, such as approximations to a circle, sometimes are used, square and rectangular arrays are by far the most predominant because of their ease of implementation.The simplest from of T is when the neighborhood is of size 1×1 (that is, a single pixel). In this case, g depends only on the value of f at (x, y), and T becomes a gray-level (also called an intensity or mapping) transformation function of the form=(3.1-2)s T r()where, for simplicity in notation, r and s are variables denoting, respectively, the grey level of f(x, y) and g(x, y)at any point (x, y).Some fairly simple, yet powerful, processing approaches can be formulates with gray-level transformations. Because enhancement at any point in an image depends only on the grey level at that point, techniques in this category often are referred to as point processing.Larger neighborhoods allow considerably more flexibility. The general approach is to use a function of the values of f in a predefined neighborhood of (x, y) to determine the value of g at (x, y). One of the principal approaches in this formulation is based on the use of so-called masks (also referred to as filters, kernels, templates, or windows). Basically, a mask is a small (say, 3×3) 2-Darray, in which the values of the mask coefficients determine the nature of the type of approach often are referred to as mask processing or filtering. These concepts are discussed in Section 3.5.3.2 Some Basic Gray Level TransformationsWe begin the study of image enhancement techniques by discussing gray-level transformation functions. These are among the simplest of all image enhancement techniques. The values of pixels, before and after processing, will be denoted by r and s, respectively. As indicated in the previous section, these values are related by an expression of the from s = T(r), where T is a transformation that maps a pixel value r into a pixel value s. Since we are dealing with digital quantities, values of the transformation function typically are stored in a one-dimensional array and the mappings from r to s are implemented via table lookups. For an 8-bit environment, a lookup table containing the values of T will have 256 entries.As an introduction to gray-level transformations, which shows three basic types of functions used frequently for image enhancement: linear (negative and identity transformations), logarithmic (log and inverse-log transformations), and power-law (nth power and nth root transformations). The identity function is the trivial case in which out put intensities are identical to input intensities. It is included in the graph only for completeness.3.2.1 Image NegativesThe negative of an image with gray levels in the range [0, L-1]is obtained by using the negative transformation show shown, which is given by the expression=--(3.2-1)s L r1Reversing the intensity levels of an image in this manner produces the equivalent of a photographic negative. This type of processing is particularly suited for enhancing white or grey detail embedded in dark regions of an image, especiallywhen the black areas are dominant in size.3.2.2 Log TransformationsThe general from of the log transformation is=+(3.2-2)log(1)s c rWhere c is a constant, and it is assumed that r ≥0 .The shape of the log curve transformation maps a narrow range of low gray-level values in the input image into a wider range of output levels. The opposite is true of higher values of input levels. We would use a transformation of this type to expand the values of dark pixels in an image while compressing the higher-level values. The opposite is true of the inverse log transformation.Any curve having the general shape of the log functions would accomplish this spreading/compressing of gray levels in an image. In fact, the power-law transformations discussed in the next section are much more versatile for this purpose than the log transformation. However, the log function has the important characteristic that it compresses the dynamic range of image characteristics of spectra. It is not unusual to encounter spectrum values that range from 0 to 106 or higher. While processing numbers such as these presents no problems for a computer, image display systems generally will not be able to reproduce faithfully such a wide range of intensity values .The net effect is that a significant degree of detail will be lost in the display of a typical Fourier spectrum.3.2.3 Power-Law TransformationsPower-Law transformations have the basic froms crϒ=(3.2-3) Where c and y are positive constants .Sometimes Eq. (3.2-3) is written as to account for an offset (that is, a measurable output when the input is zero). However, offsets typically are an issue of display calibration and as a result they are normally ignored in Eq. (3.2-3). Plots of s versus r for various values of y are shown in Fig.3.6. As in the case of the log transformation, power-law curves with fractional values of y map a narrow range of dark input values into a wider range of output values, with theopposite being true for higher values of input levels. Unlike the log function, however, we notice here a family of possible transformation curves obtained simply by varying y. As expected, we see in Fig.3.6 that curves generated with values of y＞1 have exactly the opposite effect as those generated with values of y＜1. Finally, we note that Eq.(3.2-3) reduces to the identity transformation when c = y = 1.A variety of devices used for image capture, printing, and display respond according to as gamma[hence our use of this symbol in Eq.(3.2-3)].The process used to correct this power-law response phenomena is called gamma correction.Gamma correction is important if displaying an image accurately on a computer screen is of concern. Images that are not corrected properly can look either bleached out, or, what is more likely, too dark. Trying to reproduce colors accurately also requires some knowledge of gamma correction because varying the value of gamma correcting changes not only the brightness, but also the ratios of red to green to blue. Gamma correction has become increasingly important in the past few years, as use of digital images for commercial purposes over the Internet has increased. It is not Internet has increased. It is not unusual that images created for a popular Web site will be viewed by millions of people, the majority of whom will have different monitors and/or monitor settings. Some computer systems even have partial gamma correction built in. Also, current image standards do not contain the value of gamma with which an image was created, thus complicating the issue further. Given these constraints, a reasonable approach when storing images in a Web site is to preprocess the images with a gamma that represents in a Web site is to preprocess the images with a gamma that represents an “average” of the types of monitors and computer systems that one expects in the open market at any given point in time.3.2.4 Piecewise-Linear Transformation FunctionsA complementary approach to the methods discussed in the previous three sections is to use piecewise linear functions. The principal advantage of piecewise linear functions over the types of functions we have discussed thus far is that the form of piecewise functions can be arbitrarily complex. In fact, as we will see shortly, a practical implementation of some important transformations can be formulated onlyas piecewise functions. The principal disadvantage of piecewise functions is that their specification requires considerably more user input.Contrast stretchingOne of the simplest piecewise linear functions is a contrast-stretching transformation. Low-contrast images can result from poor illumination, lack of dynamic range in the imaging sensor, or even wrong setting of a lens aperture during image acquisition. The idea behind contrast stretching is to increase the dynamic range of the gray levels in the image being processed.Gray-level slicingHighlighting a specific range of gray levels in an image often is desired. Applications include enhancing features such as masses of water in satellite imagery and enhancing flaws in X-ray images. There are several ways of doing level slicing, but most of them are variations of two basic themes. One approach is to display a high value for all gray levels in the range of interest and a low value for all other gray levels.Bit-plane slicingInstead of highlighting gray-level ranges, highlighting the contribution made to total image appearance by specific bits might be desired. Suppose that each pixel in an image is represented by 8 bits. Imagine that the image is composed of eight 1-bit planes, ranging from bit-plane 0 for the least significant bit to bit-plane 7 for the most significant bit. In terms of 8-bit bytes, plane 0 contains all the lowest order bits in the bytes comprising the pixels in the image and plane 7 contains all the high-order bits.3.3 Histogram ProcessingThe histogram of a digital image with gray levels in the range [0, L-1] is a discrete function , where is the kth gray level and is the number of pixels in the image having gray level . It is common practice to pixels in the image, denoted by n. Thus, a normalized histogram is given by , for , Loosely speaking, gives an estimate of the probability of occurrence of gray level . Note that the sum of all components of a normalized histogram is equal to 1.Histograms are the basis for numerous spatial domain processing techniques.Histogram manipulation can be used effectively for image enhancement, as shown in this section. In addition to providing useful image statistics, we shall see in subsequent chapters that the information inherent in histograms also is quite useful in other image processing applications, such as image compression and segmentation. Histograms are simple to calculate in software and also lend themselves to economic hardware implementations, thus making them a popular tool for real-time image processing.附录B第三章空间域图像增强增强的首要目标是处理图像，使其比原始图像格式和特定应用。
CCD图像图像处理外文文献翻译、中英文翻译、外文翻译
附录附录1翻译部分Raw CCD images are exceptional but not perfect. Due to the digital nature of the data many of the imperfections can be compensated for or calibrated out of the final image through digital image processing.Composition of a Raw CCD Image.A raw CCD image consists of the following signal components:IMAGE SIGNAL - The signal from the source.Electrons are generated from the actual source photons.BIAS SIGNAL - Initial signal already on the CCD before the exposure is taken. This signal is due to biasing the CCD offset slightly above zero A/D counts (ADU).THERMAL SIGNAL - Signal (Dark Current thermal electrons) due to the thermal activity of the semiconductor. Thermal signal is reduced by cooling of the CCD to low temperature.Sources of NoiseCCD images are susceptible to the following sources of noise:PHOTON NOISE - Random fluctuations in the photon signal of the source. The rate at which photons are received is not constant.THERMAL NOISE - Statistical fluctuations in the generation of Thermal signal. The rate at which electrons are produced in the semiconductor substrate due to thermal effects is not constant.READOUT NOISE - Errors in reading the signal; generally dominated by theon-chip amplifier.QUANTIZATION NOISE - Errors introduced in the A/D conversion process.SENSITIVITY VARIATION - Sensitivity variations from photosite to photosite on the CCD detector or across the detector. Modern CCD's are uniform to better than 1%between neighboring photosites and uniform to better than 10% across the entire surface.Noise CorrectionsREDUCING NOISE - Readout Noise and Quantization Noise are limited by the construction of the CCD camera and can not be improved upon by the user. Thermal Noise, however, can be reduced by cooling of the CCD (temperature regulation). The Sensitivity Variations can be removed by proper flat fielding.CORRECTING FOR THE BIAS AND THERMAL SIGNALS - The Bias and Thermal signals can be subtracted out from the Raw Image by taking what is called a Dark Exposure. The dark exposure is a measure of the Bias Signal and Thermal Signal and may simply be subtracted from the Raw Image.FLAT FIELDING -A record of the photosite to photosite sensitivity variations can be obtained by taking an exposure of a uniformly lit 'flat field". These variations can then be divided out of the Raw Image to produce an image essentially free from this source of error. Any length exposure will do, but ideally one which saturates the pixels to the 50% or 75% level is best.The Final Processed ImageThe final Processed Image which removes unwanted signals and reduces noise as best we can is computed as follows:Final Processed Image = (Raw - Dark)/FlatAll of the digital image processing functions described above can be accomplished by using CCDOPS software furnished with each SBIG imaging camera. The steps to accomplish them are described in the Operating Manual furnished with each SBIG imaging camera. At SBIG we offer our technical support to help you with questions on how to improve your images.HOW TO SELECT THE CORRECT CCD IMAGING CAMERA FOR YOUR TELESCOPEWhen new customers contact SBIG we discuss their imaging camera application. We try to get an idea of their interests. We have found this method is an effective way of insuring that our customers get the right imaging camera for their purposes. Someof the questions we ask are as follows:What type of telescope do you presently own? Having this information allows us to match the CCD imaging Camera's parameters, pixel size and field of view to your telescope. We can also help you interface the CCD imaging camera's automatic guiding functions to your telescope.Are you a MAC or PC user? Since our software supports both of these platforms we can insure that you receive the correct software. We can also answer questions about any unique functions in one or the other. We can send you a demonstration copy of the appropriate software for your review.Do you have a telescope drive base with an autoguider port? Do you want to operate from a remote computer? Companies like Software Bisque fully support our products with telescope control and imaging camera software.Do you want to take photographic quality images of deep space objects, image planets, or perform wide field searches for near earth asteroids or supernovas? In learning about your interests we can better guide you to the optimum CCD pixel size and imaging area for the application.Do you want to make photometric measurements of variable stars or determine precise asteroid positions? From this information we can recommend a CCD imaging camera model and explain how to use the specific analysis functions to perform these tasks. We can help you characterize your imaging camera by furnishing additional technical data.Do you want to automatically guide long uninterrupted astrophotographs? As the company with the most experience in CCD autoguiding we can help you install and operate a CCD autoguider on your telescope. The Model STV has a worldwide reputation for accurate guiding on dim guide stars. No matter what type of telescope you own we can help you correctly interface it and get it working properly.SBIG CCD IMAGING CAMERASThe SBIG product line consists of a series of thermoelectrically cooled CCD imaging cameras designed for a wide range of applications ranging from astronomy, tricolor imaging, color photometry, spectroscopy, medical imaging, densitometry, to chemiluminescence and epifluorescence imaging, etc. This catalog includes information on astronomical imaging cameras, scientific imaging cameras,autoguiding, and accessories. We have tried to arrange the catalog so that it is easy to compare products by specifications and performance. The tables in the product section compare some of the basic characteristics on each CCD imaging camera in our product line. You will find a more detailed set of specifications with each individual imaging camera description.HOW TO GET STARTED USING YOUR CCD IMAGING CAMERAIt all starts with the software. If there's any company well known for its outstanding imaging camera software it's SBIG. Our CCDOPS Operating Software is well known for its user oriented camera control features and stability. CCDOPS is available for free download from our web site along with sample images that you can display and analyze using the image processing and analysis functions of the CCDOPS software. You can become thoroughly familiar with how our imaging cameras work and the capabilities of the software before you purchase an imaging camera. We also include CCDSoftV5 and TheSky from Software Bisque with most of our cameras at no additional charge. Macintosh users receive a free copy of EquinoX planetarium and camera control software for the MacOS-X operating system. No other manufacturer offers better software than you get with SBIG cameras. New customers receiving their CCD imaging camera should first read the installation section in their CCDOPS Operating Manual. Once you have read that section you should have no difficulty installing CCDOPS software on your hard drive, connecting the USB cable from the imaging camera to your computer, initiating the imaging camera and within minutes start taking your first CCD images. Many of our customers are amazed at how easy it is to start taking images. Additional information can be found by reading the image processing sections of the CCDOPS and CCDSoftV5 Manuals. This information allows you to progress to more advanced features such as automatic dark frame subtraction of images, focusing the imaging camera, viewing, analyzing and processing the images on the monitor, co-adding images, taking automatic sequences of images, photometric and astrometric measurements, etc.A PERSONAL TOUCH FROM SBIGAt SBIG we have had much success with a program in which we continually review customer's images sent to us on disk or via e-mail. We can often determine the cause of a problem from actual images sent in by a user. We review the images and contacteach customer personally. Images displaying poor telescope tracking, improper imaging camera focus, oversaturated images, etc., are typical initial problems. We will help you quickly learn how to improve your images. You can be assured of personal technical support when you need it. The customer support program has furnished SBIG with a large collection of remarkable images. Many customers have had their images published in SBIG catalogs, ads, and various astronomy magazines. We welcome the chance to review your images and hope you will take advantage of our trained staff to help you improve your images.TRACK AND ACCUMULATE (U.S. Patent # 5,365,269)Using an innovative engineering approach SBIG developed an imaging camera function called Track & Accumulate (TRACCUM) in which multiple images are automatically registered to create a single long exposure. Since the long exposure consists of short images the total combined exposure significantly improves resolution by reducing the cumulative telescope periodic error. In the TRACCUM mode each image is shifted to correct guiding errors and added to the image buffer. In this mode the telescope does not need to be adjusted. The great sensitivity of the CCD virtually guarantees that there will be a usable guide star within the field of view. This feature provides dramatic improvement in resolution by reducing the effect of periodic error and allowing unattended hour long exposures. SBIG has been granted U.S. Patent # 5,365,269 for Track & Accumulate.DUAL CCD SELF-GUIDING (U.S. Patent # 5,525,793)In 1994 with the introduction of Models ST-7 and ST-8 CCD Imaging Cameras which incorporate two separate CCD detectors, SBIG was able to accomplish the goal of introducing a truly self-guided CCD imaging camera. The ability to select guide stars with a separate CCD through the full telescope aperture is equivalent to having a thermoelectrically cooled CCD autoguider in your imaging camera. This feature has been expanded to all dual sensor ST series cameras (ST-7/8/9/10/2000) and all STL series cameras (STL-1001/1301/4020/6303/11000). One CCD is used for guiding and the other for collecting the image. They are mounted in close proximity, both focused at the same plane, allowing the imaging CCD to integrate while the PC uses the guiding CCD to correct the telescope. Using a separate CCD for guiding allows 100% of the primary CCD's active area to be used to collect the image. The telescope correction rate and limiting guide star magnitude can be independentlyselected. Tests at SBIG indicated that 95% of the time a star bright enough for guiding will be found on a TC237 tracking CCD without moving the telescope, using an f/6.3 telescope. The self-guiding function quickly established itself as the easiest and most accurate method for guiding CCD images. Placing both detectors in close proximity at the same focal plane insures the best possible guiding. Many of the long integrated exposures now being published are taken with this self-guiding method, producing very high resolution images of deep space objects. SBIG has been granted U.S. Patent # 5,525,793 for the dual CCD Self-Guiding function.COMPUTER PLATFORMSSBIG has been unique in its support of both PC and Macintosh platforms for our cameras. The imaging cameras in this catalog communicate with the host computer through standard serial or USB ports depending on the specific models. Since there are no external plug-in boards required with our imaging camera systems we encourage users to operate with the new family of high resolution graphics laptop computers. We furnish Operating Software for you to install on your host computer. Once the software is installed and communication with the imaging camera is set up complete control of all of the imaging camera functions is through the host computer keyboard. The recommended minimum requirements for memory and video graphics are as shown below.GENERAL CONCLUSION(1) of this item from the theoretical analysis of the use of CCD technology for real-time non-contact measuring the diameter of the feasibility of measuring it is fast, efficient, accurate, high degree of automation, off-production time and so on.(2) projects to test the use of CCD technology to achieve real-time, online non-contact measurement, developed by the CCD-line non-contact diameter measurement system has a significant technology advanced and practical application of significance. (3) from the theoretical and experimental project on the summary of the utilization of CCD technology developed by SCM PV systems improve the measurement accuracy of several ways: improving crystal, a multi-pixel CCD devices and take full advantage of CCD-like device Face width.译文原料CCD图像是例外，但并非十全十美。
图像处理外文翻译
英文资料翻译Image processing is not a one step process．We are able to distinguish between several steps which must be performed one after the other until we can extract the data of interest from the observed scene．In this way a hierarchical processing scheme is built up as sketched in Fig．The figure gives an overview of the different phases of image processing.Image processing begins with the capture of an image with a suitable，not necessarily optical，acquisition system．In a technical or scientific application，we may choose to select an appropriate imaging system．Furthermore，we can set up the illumination system，choose the best wavelength range，and select other options to capture the object feature of interest in the best way in an image．Once the image is sensed，it must be brought into a form that can be treated with digital computers.This process is called digitization.With the problems of traffic are more and more serious. Thus Intelligent Transport System (ITS) comes out. The subject of the automatic recognition of license plate is one of the most significant subjects that are improved from the connection of computer vision and pattern recognition. The image imputed to the computer is disposed and analyzed in order to localization the position and recognition the characters on the license plate express these characters in text string form The license plate recognition system (LPSR) has important application in ITS. In LPSR, the first step is for locating the license plate in the captured image which is very important for character recognition. The recognition correction rate of license plate is governed by accurate degree of license plate location. In this paper, several of methods in image manipulation are compared and analyzed, then come out the resolutions for localization of the car plate. The experiences show that the good result has been got with these methods. The methods based on edge map and frequency analysis is used in the process of the localization of the license plate, that is to say, extracting the characteristics of the license plate in the car images after being checked up for the edge, and then analyzing and processing until the probably area of license plate is extracted.The automated license plate location is a part of the image processing ,it’s also an important part in the intelligent traffic system．It is the key step in the Vehicle License Plate Recognition(LPR).A method for the recognition of images of different backgrounds and different illuminations is proposed in the paper.the upper and lower borders are determined through the gray variation regulation of the character distribution.The left and right borders are determined through the black-white variation of the pixels in every row.The first steps of digital processing may include a number of different operations and are known as image processing．If the sensor has nonlinear characteristics, these need to be corrected．Likewise，brightness and contrast of the image may require improvement．Commonly，too，coordinate transformations are needed torestore geometrical distortions introduced during image formation．Radiometric and geometric corrections are elementary pixel processing operations．It may be necessary to correct known disturbances in the image，for instance caused by a defocused optics，motion blur，errors in the sensor，or errors in the transmission of image signals．We also deal with reconstruction techniques which are required with many indirect imaging techniques such as tomography that deliver no direct image.A whole chain of processing steps is necessary to analyze and identify objects．First，adequate filtering procedures must be applied in order to distinguish the objects of interest from other objects and the background．Essentially，from an image （or several images），one or more feature images are extracted．The basic tools for this task are averaging and edge detection and the analysis of simple neighborhoods and complex patterns known as texture in image processing．An important feature of an object is also its motion．Techniques to detect and determine motion are necessary．Then the object has to be separated from the background．This means that regions of constant features and discontinuities must be identified．This process leads to a label image．Now that we know the exact geometrical shape of the object，we can extract further information such as the mean gray value，the area，perimeter，and other parameters for the form of the object[3]．These parameters can be used to classify objects．This is an important step in many applications of image processing，as the following examples show：In a satellite image showing an agricultural area，we would like to distinguish fields with different fruits and obtain parameters to estimate their ripeness or to detect damage by parasites．There are many medical applications where the essential problem is to detect pathologi-al changes．A classic example is the analysis of aberrations in chromosomes．Character recognition in printed and handwritten text is another example which has been studied since image processing began and still poses significant difficulties．You hopefully do more，namely try to understand the meaning of what you are reading．This is also the final step of image processing，where one aims to understand the observed scene．We perform this task more or less unconsciously whenever we use our visual system．We recognize people，we can easily distinguish between the image of a scientific lab and that of a living room，and we watch the traffic to cross a street safely．We all do this without knowing how the visual system works．For some times now，image processing and computer-graphics have been treated as two different areas．Knowledge in both areas has increased considerably and more complex problems can now be treated．Computer graphics is striving to achieve photorealistic computer-generated images of three-dimensional scenes，while image processing is trying to reconstruct one from an image actually taken with a camera．In this sense，image processing performs the inverse procedure to that of computer graphics．We start with knowledge of the shape and features of an object—at the bottom of Fig. and work upwards until we get a two-dimensional image．To handle image processing or computer graphics，we basically have to work from the sameknowledge．We need to know the interaction between illumination and objects，how a three-dimensional scene is projected onto an image plane，etc．There are still quite a few differences between an image processing and a graphics workstation．But we can envisage that，when the similarities and interrelations between computergraphics and image processing are better understood and the proper hardware is developed，we will see some kind of general-purpose workstation in the future which can handle computer graphics as well as image processing tasks[5]．The advent of multimedia，i. e. ，the integration of text，images，sound，and movies，will further accelerate the unification of computer graphics and image processing.In January 1980 Scientific American published a remarkable image called Plume2，the second of eight volcanic eruptions detected on the Jovian moon by the spacecraft V oyager 1 on 5 March 1979．The picture was a landmark image in interplanetary exploration—the first time an erupting volcano had been seen in space．It was also a triumph for image processing．Satellite imagery and images from interplanetary explorers have until fairly recently been the major users of image processing techniques，where a computer image is numerically manipulated to produce some desired effect-such as making a particular aspect or feature in the image more visible.Image processing has its roots in photo reconnaissance in the Second World War where processing operations were optical and interpretation operations were performed by humans who undertook such tasks as quantifying the effect of bombing raids．With the advent of satellite imagery in the late 1960s，much computer-based work began and the color composite satellite images，sometimes startlingly beautiful, have become part of our visual culture and the perception of our planet．Like computer graphics，it was until recently confined to research laboratories which could afford the expensive image processing computers that could cope with the substantial processing overheads required to process large numbers of high-resolution images．With the advent of cheap powerful computers and image collection devices like digital cameras and scanners，we have seen a migration of image processing techniques into the public domain．Classical image processing techniques are routinely employed by graphic designers to manipulate photographic and generated imagery，either to correct defects，change color and so on or creatively to transform the entire look of an image by subjecting it to some operation such as edge enhancement.A recent mainstream application of image processing is the compression of images—either for transmission across the Internet or the compression of moving video images in video telephony and video conferencing．Video telephony is one of the current crossover areas that employ both computer graphics and classical image processing techniques to try to achieve very high compression rates．All this is part of an inexorable trend towards the digital representation of images．Indeed that most powerful image form of the twentieth century—the TV image—is also about to be taken into the digital domain．Image processing is characterized by a large number of algorithms that are specific solutions to specific problems．Some are mathematical or context-independent operations that are applied to each and every pixel．For example，we can use Fourier transforms to perform image filtering operations．Others are“algorithmic”—we may use a complicated recursive strategy to find those pixels that constitute the edges in an image．Image processing operations often form part of a computer vision system．The input image may be filtered to highlight or reveal edges prior to a shape detection usually known as low-level operations．In computer graphics filtering operations are used extensively to avoid abasing or sampling artifacts．中文翻译图像处理不是一步就能完成的过程。
图像处理(中英文)
天津大学《图像处理》课程教学大纲课程代码：2160063 课程名称：图像处理学时：32 学分： 2学时分配：授课：24 上机：实验：8 实践：实践（周）：授课学院：计算机学院更新时间：2011.6适用专业：计算机科学系先修课程：一、课程的性质与目的《数字图像处理》是计算机科学系的一门专业必选课。
本课程主要介绍数字图像处理的一些基本概念、图像基础知识、图像增强和图象分割等基本内容。
二、教学基本要求通过本课程学习，使学生了解数字图像处理的特点，掌握基本的图像处理方法，为后续模式识别等课程以及进一步学习打下理论基础。
三、教学内容1、图像基础知识（1）．了解数字图像处理的特点和主要内容。
（2）．了解数字图像处理的应用与发展。
（3）．理解视觉模型和人眼的视觉特性。
（4）．了解一些基本的数字图像的概念。
2、图像空域增强（1）．掌握灰度级线性变换、分段线性变换等图像对比度增强的方法。
（2）．掌握直方图均衡化方法、中值滤波和加权平均等平滑算法。
（3）．理解图像频域增强方法以及其与时域增强方法的联系与区别, 理解图像锐化的意义，掌握Robert梯度法，了解同态滤波等技术。
3、图象分割（1）．理解图象分割技术。
（2）．理解图象的目标表达和描述的基本概念4.数学形态学（1）.理解并掌握基本形态学运算。
（2）.利用形态学运算能够实现图像增强、图像分析等操作。
5.附录-彩色图像基本知识(1).了解彩色图像的表示及颜色空间。
(2).了解彩色图像的处理方法。
四、学时分配五、评价与考核方式实验：80%平时成绩：20%六、教材与主要参考资料教材：《图像工程（上册）：图像处理和分析》，章毓晋编著，北京：清华大学出版社，1999, 3.主要参考资料：1，《数字图像处理（第二版）》，冈萨雷斯编著，电子工业出版社，2003,32，数字图像处理基础，朱虹，科学出版社，2005,10TU Syllabus for Digital Image ProcessingCode: 2160063Title:Digital ImageProcessing Semester Hours: 24 Credits: 2Semester Hour Structure Lecture：24 Computer Lab：Experiment：8 Practice：Practice (Week)：Offered by: School of CS Date: 2011.6for: Department of Computer SciencePrerequisite:1. Objective“Digital Image Processing”is a selective courses of specialized subject of department of computer science in school of CS. The main contents of this course includes the basic concepts of digital image, fundamentals, image enhancement and image segmentation etc.2. Course DescriptionThe students should know about the characteristics of digital image processing and comprehend the basic image processing methods, which can be used in further study, such as pattern recognition.3. Topics1、fundamentals in digital image（1）．Know about the main contents and characteristics of image processing. （2）．Know about the applications and development of image processing. （3）．Comprehend vision model and human vision characteristics.（4）．Know about some basic concepts of digital image.2、image enhancement in spacial domain（1）．Comprehend the basic image contrast enhancement method. （2）．Comprehend the basic image smoothing method, such as median filter, KNN, etc.（3）．Comprehend the image enhancement methods in frequency domain3、image segmentation（1）．Comprehend and realize the basic image segmentation technologys. （2）．Comprehend the target representation and description.4.（1）. comprehend and be able to use fundamental operation of mathematical morphology.（2）. be able to realize image enhancement and image analysis using different operations of mathematical morphology.5. appendix-(1).know about color image representation and the color space.(2).know about the basic color image processing methods.4. Semester Hour StructureExperiment: 80%Attendance: 20%6. Text-Book & Additional ReadingsText-book:Image Engineering（1st volume）：image processing and analysis，Zhang Yujin，Tsinghua University Press，1999, 3.Additional Readings：1，Digital image processing（2nd edition），Gonzalez，Electric Industry Press,2003,3 2，Digital image processing fundamental，Zhu Hong，China Science Press，2005.10。
图像处理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)译文：基于局部二值模式多分辨率的灰度和旋转不变性的纹理分类摘要：本文描述了理论上非常简单但非常有效的，基于局部二值模式的、样图的非参数识别和原型分类的，多分辨率的灰度和旋转不变性的纹理分类方法。
此方法是基于结合某种均衡局部二值模式，是局部图像纹理的基本特性，并且已经证明生成的直方图是非常有效的纹理特征。
我们获得一个一般灰度和旋转不变的算子，可表达检测有角空间和空间结构的任意量子化的均衡模式，并提出了结合多种算子的多分辨率分析方法。
根据定义，该算子在图像灰度发生单一变化时具有不变性，所以所提出的方法在灰度发生变化时是非常强健的。
另一个优点是计算简单，算子在小邻域内或同一查找表内只要几个操作就可实现。
在旋转不变性的实际问题中得到了良好的实验结果,与来自其他的旋转角度的样品一起以一个特别的旋转角度试验而且测试得到分类, 证明了基于简单旋转的发生统计学的不变性二值模式的分辨是可以达成。
这些算子表示局部图像纹理的空间结构的又一特色是，由结合所表示的局部图像纹理的差别的旋转不变量不一致方法，其性能可得到进一步的改良。
这些直角的措施共同证明了这是旋转不变性纹理分析的非常有力的工具。
关键词：非参数的，纹理分析，Outex ，Brodatz ，分类，直方图，对比度2 灰度和旋转不变性的局部二值模式我们通过定义单色纹理图像的一个局部邻域的纹理T ，如 P （P>1）个象素点的灰度级联合分布，来描述灰度和旋转不变性算子：01(,,)c P T t g g g -= （1）其中，g c 为局部邻域中心像素点的灰度值，g p （p=0，1…P-1）为半径R(R>0)的圆形邻域内对称的空间象素点集的灰度值。
图1如果g c 的坐标是（0,0），那么g p 的坐标为(cos sin(2/),(2/))R R p P p P ππ-。
图1举例说明了圆形对称邻域集内各种不同的（P,R ）。
计算机视觉常用术语中英文对照
计算机视觉常用术语中英文对照（1）2011-06-08 21:26人工智能 Artificial Intelligence认知科学与神经科学Cognitive Science and Neuroscience 图像处理Image Processing计算机图形学Computer graphics模式识别Pattern Recognized图像表示Image Representation立体视觉与三维重建Stereo Vision and 3D Reconstruction 物体（目标）识别Object Recognition运动检测与跟踪Motion Detection and Tracking边缘edge边缘检测detection区域region图像分割segmentation轮廓与剪影contour and silhouette纹理texture纹理特征提取feature extraction颜色color局部特征local features or blob尺度scale摄像机标定Camera Calibration立体匹配stereo matching图像配准Image Registration特征匹配features matching物体识别Object Recognition人工标注Ground-truth自动标注Automatic Annotation运动检测与跟踪Motion Detection and Tracking 背景剪除Background Subtraction背景模型与更新background modeling and update运动跟踪Motion Tracking多目标跟踪multi-target tracking颜色空间color space色调Hue色饱和度Saturation明度Value颜色不变性Color Constancy（人类视觉具有颜色不变性）照明illumination反射模型Reflectance Model明暗分析Shading Analysis成像几何学与成像物理学Imaging Geometry and Physics全像摄像机Omnidirectional Camera激光扫描仪Laser Scanner透视投影Perspective projection正交投影Orthopedic projection表面方向半球Hemisphere of Directions立体角solid angle透视缩小效应foreshortening辐射度radiance辐照度irradiance亮度intensity漫反射表面、Lambertian（朗伯）表面diffuse surface 镜面Specular Surfaces漫反射率diffuse reflectance明暗模型Shading Models环境光照ambient illumination互反射interreflection反射图Reflectance Map纹理分析Texture Analysis元素elements基元primitives纹理分类texture classification从纹理中恢复图像shape from texture 纹理合成synthetic图形绘制graph rendering图像压缩image compression统计方法statistical methods结构方法structural methods基于模型的方法model based methods 分形fractal自相关性函数autocorrelation function 熵entropy能量energy对比度contrast均匀度homogeneity上下文约束contextual constraintsGibbs随机场吉布斯随机场边缘检测、跟踪、连接Detection、Tracking、LinkingLoG边缘检测算法（墨西哥草帽算子）LoG=Laplacian of Gaussian 霍夫变化Hough Transform链码chain codeB-样条B-spline有理B-样条Rational B-spline非均匀有理B-样条Non-Uniform Rational B-Spline控制点control points节点knot points基函数basis function控制点权值weights曲线拟合curve fitting逼近approximation回归Regression主动轮廓Active Contour Model or Snake 图像二值化Image thresholding连通成分connected component数学形态学mathematical morphology结构元structuring elements膨胀Dilation腐蚀Erosion开运算opening闭运算closing聚类clustering分裂合并方法split-and-merge区域邻接图region adjacency graphs四叉树quad tree区域生长Region Growing过分割over-segmentation分水岭watered金字塔pyramid亚采样sub-sampling尺度空间Scale Space局部特征Local Features背景混淆clutter遮挡occlusion角点corners强纹理区域strongly textured areas 二阶矩阵Second moment matrix 视觉词袋bag-of-visual-words类内差异intra-class variability类间相似性inter-class similarity生成学习Generative learning判别学习discriminative learning人脸检测Face detection弱分类器weak learners集成分类器ensemble classifier被动测距传感passive sensing多视点Multiple Views稠密深度图dense depth稀疏深度图sparse depth视差disparity外极epipolar外极几何Epipolor Geometry校正Rectification归一化相关NCC Normalized Cross Correlation平方差的和SSD Sum of Squared Differences绝对值差的和SAD Sum of Absolute Difference俯仰角pitch偏航角yaw扭转角twist高斯混合模型Gaussian Mixture Model运动场motion field光流optical flow贝叶斯跟踪Bayesian tracking粒子滤波Particle Filters颜色直方图color histogram尺度不变特征转换SIFT scale invariant feature transform 孔径问题Aperture problem/view/77fb81ddad51f01dc281f1a7.html/quotes/txt/2007-09/06/content_75057.htm /message/message1.html/90001/90776/90883/7342346.html。
Photoshop中英文菜单对照表
Photoshop中英文菜单对照表ps教育 02-03 21:31---------------------------------Photoshop是Adobe公司旗下最为出名的图像处理软件之一，集图像扫描、编辑修改、图像制作、广告创意，图像输入与输出于一体的图形图像处理软件，深受广大平面设计人员和电脑美术爱好者的喜爱。
由于该软件是由美国研制的，最初的版本肯定是英文的，由于该软件十分出色，备受国内设计人员的喜爱，但最初由于语言的限制，国内只有懂得英文的人士才能勉强使用，虽然现在的版本大多数是中文的，但还是有一部分朋友已经习惯了英文，或是用英文版本出的教程，很多新手看不懂。
一、File 文件（菜单）二、Edit 编辑（菜单）1.New 新建 1.Undo 还原2.Open 打开 2.Step Forward 向前3.Open As 打开为 3.Step Backward 返回4.Open Recent 最近打开文件 4.Fade 消退5.Close 关闭 5.Cut 剪切6.Save 存储 6.Copy 拷贝7.Save As 存储为7.Copy Merged 合并拷贝8.Save for Web 存储为Web所用格式8.Paste 粘贴9.Revert 恢复9.Paste Into 粘贴入10.Place 置入10.Clear 清除1 PDF Image PDF图象导入11.Fill 填充2 Annotations 注释12.Stroke 描边12.Export 输出13.Free Transform 自由变形13.Manage Workflow 管理工作流程14.Transform 变换1 Check In 登记 1 Again 再次2 Undo Check Out 还原注销 2 Sacle 缩放3 Upload To Server 上载到服务器 3 Rotate 旋转4 Add To Workflow 添加到工作流程 4 Skew 斜切5 Open From Workflow 从工作流程打开 5 Distort 扭曲14.Automate 自动 6 Prespective 透视1 Batch 批处理7 Rotate 180° 旋转180度2 Create Droplet 创建快捷批处理8 Rotate 90°CW 顺时针旋转90度3 Conditional Mode Change 条件模式更改9 Rotate 90°CCW 逆时针旋转90度4 Contact Sheet 联系表10 Flip Hpeizontal 水平翻转5 Fix Image 限制图像11 Flip Vertical 垂直翻转6 Multi Page PDF to PSD 多页面PDF文件到15.Define Brush 定义画笔PSD文件7 Picture package 图片包16.Define Pattern 设置图案8 Web Photo Gallery Web照片画廊17.Define Custom Shape 定义自定形状15.File Info 文件简介18.Purge 清除内存数据16.Print Options 打印选项 1 Undo 还原17.Page Setup 页面设置 2 Clipboard 剪贴板18.Print 打印 3 Histories 历史纪录19.Jump to 跳转到 4 All 全部20.Exit 退出19.Color Settings 颜色设置20.Preset Manager 预置管理器21.Preferences 预设1 General 常规2 Saving Files 存储文件3 Display ＆Cursors 显示与光标4 Transparency ＆Gamut 透明区域与色域5 Units ＆Rulers 单位与标尺6 Guides ＆Grid 参考线与网格7 Plug Ins ＆Scratch Disks 增效工具与暂存盘8 Memory ＆Image Cache 内存和图像高速缓存9 Adobe Online Adobe在线10 Workflows Options 工作流程选项三、Image 图像（菜单）四、Layer 图层（菜单）1.Mode 模式 1.New 新建1 Bitmap 位图 1 Layer 图层2 Grayscale 灰度 2 Background From Layer 背景图层4.Open Recent 最近打开文件 3 Layer Set 图层组3 Duotone 双色调4 Layer Set From Linked 图层组来自链接的4 Indexed Color 索引色5 Layer via Copy 通过拷贝的图层5 RGB Color RGB色6 Layer via Cut 通过剪切的图层8.Save for Web 存储为Web所用2.Duplicate Layer 复制图层格式6 CMYK Color CMYK色 3.Delete Layer 删除图层7 Lab Color Lab色 yer Properties 图层属性8 Multichannel 多通道 yer Style 图层样式9 8 Bits/Channel 8位通道 1 Blending Options 混合选项10 16 Bits/Channel 16位通道 2 Drop Shadow 投影11 Color Table 颜色表 3 Inner Shadow 内阴影12 Assing Profile 制定配置文件 4 Outer Glow 外发光13 Convert to Profile 转换为配置5 Inner Glow 内发光文件2.Adjust 调整 6 Bevel and Emboss 斜面和浮雕1 Levels 色阶7 Satin 光泽2 Auto Laves 自动色阶8 Color Overlay 颜色叠加3 Auto Contrast 自动对比度9 Gradient Overlay 渐变叠加4 Curves 曲线10 Pattern Overlay 图案叠加5 Color Balance 色彩平衡11 Stroke 描边6 Brightness/Contrast 亮度/对比12 Copy Layer Effects 拷贝图层样式度7 Hue/Saturation 色相/饱和度13 Paste Layer Effects 粘贴图层样式14 Paste Layer Effects To Linked 将图层样式粘贴8 Desaturate 去色的链接的9 Replace Color 替换颜色15 Clear Layer Effects 清除图层样式10 Selective Color 可选颜色16 Global Light 全局光11 Channel Mixer 通道混合器17 Create Layer 创建图层12 Gradient Map 渐变映射18 Hide All Effects 显示/隐藏全部效果13 Invert 反相19 Scale Effects 缩放效果14 Equalize 色彩均化 6.New Fill Layer 新填充图层15 Threshold 阈值 1 Solid Color 纯色16 Posterize 色调分离 2 Gradient 渐变17 Variations 变化 3 Pattern 图案3.Duplicate 复制7.New Adjustment Layer 新调整图层4.Apply Image 应用图像 1 Levels 色阶5.Calculations 计算 2 Curves 曲线6.Image Size 图像大小 3 Color Balance 色彩平衡7.Canvas Size 画布大小 4 Brightness/Contrast 亮度/对比度8.Rotate Canvas 旋转画布 5 Hue/Saturation 色相/饱和度1 180° 180度 6 Selective Color 可选颜色2 90°CW 顺时针90度7 Channel Mixer 通道混合器3 90°CCW 逆时针90度8 Gradient Map 渐变映射4 Arbitrary 任意角度9 Invert 反相5 Flip Horizontal 水平翻转10 Threshold 阈值6 Flip Vertical 垂直翻转11 Posterize 色调分离9.Crop 裁切8.Change Layer Content 更改图层内容10.Trim 修整yer Content Options 图层内容选项11.Reverl All 显示全部10.Type 文字12.Histogram 直方图 1 Create Work Path 创建工作路径13.Trap 陷印 2 Convert to Shape 转变为形状14.Extract 抽出 3 Horizontal 水平15.Liquify 液化 4 Vertical 垂直5 Anti-Alias None 消除锯齿无五、Selection 选择（菜单） 6 Anti-Alias Crisp 消除锯齿明晰1.All 全部7 Anti-Alias Strong 消除锯齿强2.Deselect 取消选择8 Anti-Alias Smooth 消除锯齿平滑3.Reselect 重新选择9 Covert To Paragraph Text 转换为段落文字4.Inverse 反选10 Warp Text 文字变形5.Color Range 色彩范围11 Update All Text Layers 更新所有文本图层6.Feather 羽化12 Replace All Missing Fonts 替换所以缺欠文字7.Modify 修改11.Rasterize 栅格化1 Border 扩边 1 Type 文字2 Smooth 平滑 2 Shape 形状3 Expand 扩展 3 Fill Content 填充内容4 Contract 收缩 4 Layer Clipping Path 图层剪贴路径8.Grow 扩大选区 5 Layer 图层9.Similar 选区相似 6 Linked Layers 链接图层10.Transform Selection 变换选区7 All Layers 所以图层11.Load Selection 载入选区12.New Layer Based Slice 基于图层的切片12.Save Selection 存储选区13.Add Layer Mask 添加图层蒙板1 Reveal All 显示全部六、Filter 滤镜（菜单） 2 Hide All 隐藏全部st Filter 上次滤镜操作 3 Reveal Selection 显示选区2.Artistic 艺术效果 4 Hide Selection 隐藏选区1 Colored Pencil 彩色铅笔14.Enable Layer Mask 启用图层蒙板2 Cutout 剪贴画15.Add Layer Clipping Path 添加图层剪切路径3 Dry Brush 干笔画 1 Reveal All 显示全部4 Film Grain 胶片颗粒 2 Hide All 隐藏全部5 Fresco 壁画 3 Current Path 当前路径6 Neon Glow 霓虹灯光16.Enable Layer Clipping Path 启用图层剪切路径7 Paint Daubs 涂抹棒17.Group Linked 于前一图层编组8 Palette Knife 调色刀18.UnGroup 取消编组9 Plastic Wrap 塑料包装19.Arrange 排列10 Poster Edges 海报边缘 1 Bring to Front 置为顶层11 Rough Pastels 粗糙彩笔 2 Bring Forward 前移一层12 Smudge Stick 绘画涂抹 3 Send Backward 后移一层13 Sponge 海绵 4 Send to Back 置为底层14 Underpainting 底纹效果20.Arrange Linked 对齐链接图层15 Watercolor 水彩 1 Top Edges 顶边3.Blur 模糊 2 Vertical Center 垂直居中1 Blur 模糊 3 Bottom Edges 底边2 Blur More 进一步模糊 4 Left Edges 左边3 Gaussian Blur 高斯模糊 5 Horizontal Center 水平居中4 Motion Blur 动态模糊 6 Right Edges 右边5 Radial Blur 径向模糊21.Distribute Linked 分布链接的6 Smart Blur 特殊模糊 1 Top Edges 顶边4.Brush Strokes 画笔描边 2 Vertical Center 垂直居中1 Accented Edges 强化边缘 3 Bottom Edges 底边2 Angled Stroke 成角的线条 4 Left Edges 左边3 Crosshatch 阴影线 5 Horizontal Center 水平居中4 Dark Strokes 深色线条 6 Right Edges 右边5 Ink Outlines 油墨概况22.Lock All Linked Layers 锁定所有链接图层6 Spatter 喷笔23.Merge Linked 合并链接图层7 Sprayed Strokes 喷色线条24.Merge Visible 合并可见图层8 Sumi 总量25.Flatten Image 合并图层5.Distort 扭曲26.Matting 修边1 Diffuse Glow 扩散亮光 1 Define 去边2 Displace 置换 2 Remove Black Matte 移去黑色杂边3 Glass 玻璃 3 Remove White Matte 移去白色杂边4 Ocean Ripple 海洋波纹5 Pinch 挤压七、View 视图（菜单）6 Polar Coordinates 极坐标 1.New View 新视图7 Ripple 波纹 2.Proof Setup 校样设置8 Shear 切变 1 Custom 自定9 Spherize 球面化 2 Working CMYK 处理CMYK10 Twirl 旋转扭曲 3 Working Cyan Plate 处理青版11 Wave 波浪 4 Working Magenta Plate 处理洋红版12 Zigzag 水波 5 Working Yellow Plate 处理黄版6.Noise 杂色 6 Working Black Plate 处理黑版1 Add Noise 加入杂色7 Working CMY Plate 处理CMY版2 Despeckle 去斑8 Macintosh RGB3 Dust ＆Scratches 蒙尘与划痕9 Windows RGB4 Median 中间值10 Monitor RGB 显示器RGB7.Pixelate 像素化11 Simulate Paper White 模拟纸白1 Color Halftone 彩色半调12 Simulate Ink Black 模拟墨黑2 Crystallize 晶格化 3.Proof Color 校样颜色3 Facet 彩块化 4.Gamut Wiring 色域警告4 Fragment 碎片 5.Zoom In 放大5 Mezzotint 铜版雕刻 6.Zoom Out 缩小6 Mosaic 马赛克7.Fit on Screen 满画布显示7 Pointillize 点状化8.Actual Pixels 实际象素8.Render 渲染9.Print Size 打印尺寸1 3D Transform 3D 变换10.Show Extras 显示额外的2 Clouds 云彩11.Show 显示3 Difference Clouds 分层云彩 1 Selection Edges 选区边缘4 Lens Flare 镜头光晕 2 Target Path 目标路径5 Lighting Effects 光照效果 3 Grid 网格6 Texture Fill 纹理填充 4 Guides 参考线9.Sharpen 锐化 5 Slices 切片1 Sharpen 锐化 6 Notes 注释2 Sharpen Edges 锐化边缘7 All 全部3 Sharpen More 进一步锐化8 None 无4 Unsharp Mask USM 锐化9 Show Extras Options 显示额外选项10.Sketch 素描12.Show Rulers 显示标尺1 Bas Relief 基底凸现13.Snap 对齐2 Chalk ＆Charcoal 粉笔和炭笔14.Snap To 对齐到3 Charcoal 1 Guides 参考线4 Chrome 铬黄 2 Grid 网格5 Conte Crayon 彩色粉笔 3 Slices 切片6 Graphic Pen 绘图笔 4 Document Bounds 文档边界7 Halftone Pattern 半色调图案 5 All 全部8 Note Paper 便条纸 6 None 无9 Photocopy 副本15.Show Guides 锁定参考线10 Plaster 塑料效果16.Clear Guides 清除参考线11 Reticulation 网状17.New Guides 新参考线12 Stamp 图章18.Lock Slices 锁定切片13 Torn Edges 撕边19.Clear Slices 清除切片14 Water Paper 水彩纸11.Stylize 风格化八、Windows 窗口（菜单）1 Diffuse 扩散 1.Cascade 层叠2 Emboss 浮雕 2.Tile 拼贴3 Extrude 突出 3.Arrange Icons 排列图标4 Find Edges 查找边缘 4.Close All 关闭全部5 Glowing Edges 照亮边缘 5.Show/Hide Tools 显示/隐藏工具6 Solarize 曝光过度 6.Show/Hide Options 显示/隐藏选项7 Tiles 拼贴7.Show/Hide Navigator 显示/隐藏导航8 Trace Contour 等高线8.Show/Hide Info 显示/隐藏信息9 Wind 风9.Show/Hide Color 显示/隐藏颜色12.Texture 纹理10.Show/Hide Swatches 显示/隐藏色板1 Craquelure 龟裂缝11.Show/Hide Styles 显示/隐藏样式2 Grain 颗粒12.Show/Hide History 显示/隐藏历史记录3 Mosained Tiles 马赛克拼贴13.Show/Hide Actions 显示/隐藏动作4 Patchwork 拼缀图14.Show/Hide Layers 显示/隐藏图层5 Stained Glass 染色玻璃15.Show/Hide Channels 显示/隐藏通道6 Texturixer 纹理化16.Show/Hide Paths 显示/隐藏路径13.Video 视频17.Show/Hide Character 显示/隐藏字符1 De Interlace 逐行18.Show/Hide Paragraph 显示/隐藏段落2 NTSC Colors NTSC色彩19.Show/Hide Status Bar 显示/隐藏状态栏14.Other 其它20.Reset Palette Locations1 Custom 自定义2 High Pass 高反差保留3 Maximum 最大值4 Minimum 最小值5 Offset 位移15.Digimarc1 Embed Watermark 嵌入水印2 Read Watermark 读取水印ps教育关注PS教育学习，及时收取图文教程，为网友提供PS相关知识，PS教程每天更新，是广大网络的PS教程网爱好者学习的乐园，PS软件学习，让学习无忧。
数字图像处理中英文对照外文翻译文献
中英文翻译原文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 the 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 widespread application.Image edge detection method many and varied, in which based on brightness algorithm, is studies the time to be most long, the theory develops the maturest method, it mainly is through some difference operator, calculates its gradient based on image brightness the change, thus examines the edge, mainly has Robert, Laplacian, Sobel, Canny, operators and so on LOG。
2023年英语学习资料之Photoshop词汇中英文对照
2023年英语学习资料之Photoshop词汇中英文对照随着信息技术的快速发展，学习外语成为越来越多人追求的目标。
而对于许多科技工作者和设计师来说，学习Photoshop是非常必要的。
Photoshop是Adobe公司所开发的图像处理软件，将各种不同的编辑和设计工具集成在一个平台上，使得用户可以轻松地进行图像处理和编辑。
在学习Photoshop的过程中，词汇学习是必不可少的一部分。
以下是一些常用的Photoshop词汇及其中英文对照。
1. 操作面板 - Panel2. 菜单 - Menu3. 工具 - Tool4. 插件 - Plug-in5. 滤镜 - Filter6. 图层 - Layer7. 图像 - Image8. 图像大小 - Image Size9. 像素 - Pixel10. 位图 - Bitmap11. 向量 - Vector12. 图像解析度 - Image Resolution13. 颜色空间 - Color Space14. 亮度 - Brightness15. 对比度 - Contrast16. 饱和度 - Saturation17. 色彩平衡 - Color Balance18. 贴图 - Texture19. 抠图 - Clipping20. 裁剪 - CropPhotoshop是一个兼具艺术和科学的软件，涉及到众多的技术术语和专业的知识点。
学习这些词汇不仅可以加深我们对Photoshop软件的理解，同时还能帮助我们更加高效地使用这个软件。
在未来，随着科技的不断发展，人们对于数字媒体处理的需求将会越来越大。
因此，学习Photoshop已经成为必须掌握的技能之一。
总之，在学习Photoshop的过程中，无论是对于初学者还是有经验的用户，词汇学习都是必不可少的环节。
通过学习这些词汇，可以在更加专业地使用设计工具和优化图像的基础上，更好地应用这个软件。
未来，我们也应该更加关注数字媒体的发展趋势，探索更多新的技术与知识领域。
数字图像处理英文原版及翻译
Digital Image Processing and Edge DetectionDigital Image ProcessingInterest in digital image processing methods stems from two principal application areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for autonomous machine perception.An image may be defined as a two-dimensional function, f(x, y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pixels, and pixels. Pixel is the term most widely used to denote the elements of a digital image.Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike humans, who are limited to the visual band of the electromagnetic (EM) spec- trum, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images generated by sources that humans are not accustomed to associating with images. These include ultra- sound, electron microscopy, and computer-generated images. Thus, digital image processing encompasses a wide and varied field of applications.There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer vi- sion, start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images. We believe this to be a limiting and somewhat artificial boundary. For example, under this definition, even the trivial task of computing the average intensity of an image (which yields asingle number) would not be considered an image processing operation. On the other hand, there are fields such as computer vision whose ultimate goal is to use computers to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs. This area itself is a branch of artificial intelligence (AI) whose objective is to emulate human intelligence. The field of AI is in its earliest stages of infancy in terms of development, with progress having been much slower than originally anticipated. The area of image analysis (also called image understanding) is in be- tween image processing and computer vision.There are no clearcut boundaries in the continuum from image processing at one end to computer vision at the other. However, one useful paradigm is to consider three types of computerized processes in this continuum: low-, mid-, and high level processes. Low-level processes involve primitive opera- tions such as image preprocessing to reduce noise, contrast enhancement, and image sharpening. A low-level process is characterized by the fact that both its inputs and outputs are images. Mid-level processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects. A midlevel process is characterized by the fact that its inputs generally are images, but its outputs are attributes extracted from those images (e.g., edges, contours, and the identity of individual objects). Finally, higher level processing involves “making sense” of an ensemble of recognized objects, as in image analysis, and, at the far end of the continuum, performing the cognitive functions normally associated with vision.Based on the preceding comments, we see that a logical place of overlap between image processing and image analysis is the area of recognition of individual regions or objects in an image. Thus, what we call in this book digital image processing encompasses processes whose inputs and outputs are images and, in addition, encompasses processes that extract attributes from images, up to and including the recognition of individual objects. As a simple illustration to clarify these concepts, consider the area of automated analysis of text. The processes of acquiring an image of the area containing the text, preprocessing that image, extracting(segmenting) the individual characters, describing the characters in a form suitable for computer processing, and recognizing those individual characters are in the scope of what we call digital image processing in this book. Making sense of the content of the page may be viewed as being in the domain of image analysis and even computer vision, depending on the level of complexity implied by the statement “making sense.”As will become evident shortly, digital image processing, as we have defined it, is used successfully in a broad range of areas of exceptional social and economic value.The areas of application of digital image processing are so varied that some form of organization is desirable in attempting to capture the breadth of this field. One of the simplest ways to develop a basic understanding of the extent of image processing applications is to categorize images according to their source (e.g., visual, X-ray, and so on). The principal energy source for images in use today is the electromagnetic energy spectrum. Other important sources of energy include acoustic, ultrasonic, and electronic (in the form of electron beams used in electron microscopy). Synthetic images, used for modeling and visualization, are generated by computer. In this section we discuss briefly how images are generated in these various categories and the areas in which they are applied.Images based on radiation from the EM spectrum are the most familiar, especially images in the X-ray and visual bands of the spectrum. Electromagnet- ic waves can be conceptualized as propagating sinusoidal waves of varying wavelengths, or they can be thought of as a stream of massless particles, each traveling in a wavelike pattern and moving at the speed of light. Each massless particle contains a certain amount (or bundle) of energy. Each bundle of energy is called a photon. If spectral bands are grouped according to energy per photon, we obtain the spectrum shown in fig. below, ranging from gamma rays (highest energy) at one end to radio waves (lowest energy) at the other. The bands are shown shaded to convey the fact that bands of the EM spectrum are not distinct but rather transition smoothly from one to theother.Image acquisition is the first process. Note that acquisition could be as simple as being given an image that is already in digital form. Generally, the image acquisition stage involves preprocessing, such as scaling.Image enhancement is among the simplest and most appealing areas of digital image processing. Basically, the idea behind enhancement techniques is to bring out detail that is obscured, or simply to highlight certain features of interest in an image. A familiar example of enhancement is when we increase the contrast of an image because “it looks better.” It is important to keep in mind that enhancement is a very subjective area of image processing. Image restoration is an area that also deals with improving the appearance of an image. However, unlike enhancement, which is subjective, image restoration is objective, in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation. Enhancement, on the other hand, is based on human subjective preferences regarding what constitutes a “good”enhancement result.Color image processing is an area that has been gaining in importance because of the significant increase in the use of digital images over the Internet. It covers a number of fundamental concepts in color models and basic color processing in a digital domain. Color is used also in later chapters as the basis for extracting features of interest in an image.Wavelets are the foundation for representing images in various degrees of resolution. In particular, this material is used in this book for image data compression and for pyramidal representation, in which images are subdivided successively into smaller regions.Compression, as the name implies, deals with techniques for reducing the storage required to save an image, or the bandwidth required to transmit it.Although storage technology has improved significantly over the past decade, the same cannot be said for transmission capacity. This is true particularly in uses of the Internet, which are characterized by significant pictorial content. Image compression is familiar (perhaps inadvertently) to most users of computers in the form of image , such as the jpg used in the JPEG (Joint Photographic Experts Group) image compression standard.Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape. The material in this chapter begins a transition from processes that output images to processes that output image attributes.Segmentation procedures partition an image into its constituent parts or objects. In general, autonomous segmentation is one of the most difficult tasks in digital image processing. A rugged segmentation procedure brings the process a longway toward successful solution of imaging problems that require objects to be identified individually. On the other hand, weak or erratic segmentation algorithms almost always guarantee eventual failure. In general, the more accurate the segmentation, the more likely recognition is to succeed.Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the boundary of a region (i.e., the set of pixels separating one image region from another) or all the points in the region itself. In either case, converting the data to a form suitable for computer processing is necessary. The first decision that must be made is whether the data should be represented as a boundary or as a complete region. Boundary representation is appropriate when the focus is on external shape characteristics, such as corners and inflections. Regional representation is appropriate when the focus is on internal properties, such as texture or skeletal shape. In some applications, these representations complement each other. Choosing a representation is only part of the solution for trans- forming raw data into a form suitable for subsequent computer processing. A method must also be specified for describing the data so that features of interest are highlighted. Description, also called feature selection, deals with extracting attributes that result in some quantitative information of interest or are basic for differentiating one class of objects from another.Recognition is the process that assigns a label (e.g., “vehicle”) to an object based on its descriptors. As detailed before, we conclude our coverage of digital image processing with the development of methods for recognition of individual objects.So far we have said nothing about the need for prior knowledge or about the interaction between the knowledge base and the processing modules in Fig 2 above. Knowledge about a problem domain is coded into an image processing system in the form of a knowledge database. This knowledge may be as simple as detailing regions of an image where theinformation of interest is known to be located, thus limiting the search that has to be conducted in seeking that information. The knowledge base also can be quite complex, such as an interrelated list of all major possible defects in a materials inspection problem or an image database containing high-resolution satellite images of a region in connection with change-detection applications. In addition to guiding the operation of each processing module, the knowledge base also controls the interaction between modules. This distinction is made in Fig 2 above by the use of double-headed arrows between the processing modules and the knowledge base, as opposed to single-headed arrows linking the processing modules.Edge detectionEdge detection is a terminology in image processing and computer vision, particularly in the areas of feature detection and feature extraction, to refer to algorithms which aim at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities.Although point and line detection certainly are important in any discussion on segmentation,edge detection is by far the most common approach for detecting meaningful discounties in gray level.Although certain literature has considered the detection of ideal step edges, the edges obtained from natural images are usually not at all ideal step edges. Instead they are normally affected by one or several of the following effects:1.focal blur caused by a finite depth-of-field and finite point spread function; 2.penumbral blur caused by shadows created by light sources of non-zero radius; 3.shading at a smooth object edge; 4.local specularities or interreflections in the vicinity of object edges.A typical edge might for instance be the border between a block of red color and a block of yellow. In contrast a line (as can be extracted by a ridge detector) can be a small number of pixels of a different color on an otherwise unchanging background. For a line, there maytherefore usually be one edge on each side of the line.To illustrate why edge detection is not a trivial task, let us consider the problem of detecting edges in the following one-dimensional signal. Here, we may intuitively say that there should be an edge between the 4th and 5th pixels.If the intensity difference were smaller between the 4th and the 5th pixels and if the intensity differences between the adjacent neighbouring pixels were higher, it would not be as easy to say that there should be an edge in the corresponding region. Moreover, one could argue that this case is one in which there are several edges.Hence, to firmly state a specific threshold on how large the intensity change between two neighbouring pixels must be for us to say that there should be an edge between these pixels is not always a simple problem. Indeed, this is one of the reasons why edge detection may be a non-trivial problem unless the objects in the scene are particularly simple and the illumination conditions can be well controlled.There are many methods for edge detection, but most of them can be grouped into two categories,search-based and zero-crossing based. The search-based methods detect edges by first computing a measure of edge strength, usually a first-order derivative expression such as the gradient magnitude, and then searching for local directional maxima of the gradient magnitude using a computed estimate of the local orientation of the edge, usually the gradient direction. The zero-crossing based methods search for zero crossings in a second-order derivative expression computed from the image in order to find edges, usually the zero-crossings of the Laplacian of the zero-crossings of a non-linear differential expression, as will be described in the section on differential edge detection following below. As a pre-processing step to edge detection, a smoothing stage, typically Gaussian smoothing, is almost always applied (see also noise reduction).The edge detection methods that have been published mainly differ in the types of smoothing filters that are applied and the way the measures of edge strength are computed. As many edge detection methods rely on the computation of image gradients, they also differ in the types of filters used for computing gradient estimates in the x- and y-directions.Once we have computed a measure of edge strength (typically the gradient magnitude), the next stage is to apply a threshold, to decide whether edges are present or not at an image point. The lower the threshold, the more edges will be detected, and the result will be increasingly susceptible to noise, and also to picking out irrelevant features from the image. Conversely a high threshold may miss subtle edges, or result in fragmented edges.If the edge thresholding is applied to just the gradient magnitude image, the resulting edges will in general be thick and some type of edge thinning post-processing is necessary. For edges detected with non-maximum suppression however, the edge curves are thin by definition and the edge pixels can be linked into edge polygon by an edge linking (edge tracking) procedure. On a discrete grid, the non-maximum suppression stage can be implemented by estimating the gradient direction using first-order derivatives, then rounding off the gradient direction to multiples of 45 degrees, and finally comparing the values of the gradient magnitude in the estimated gradient direction.A commonly used approach to handle the problem of appropriate thresholds for thresholding is by using thresholding with hysteresis. This method uses multiple thresholds to find edges. We begin by using the upper threshold to find the start of an edge. Once we have a start point, we then trace the path of the edge through the image pixel by pixel, marking an edge whenever we are above the lower threshold. We stop marking our edge only when the value falls below our lower threshold. This approach makes the assumption that edges are likely to be in continuous curves, and allows us to follow a faint section of an edge we have previously seen, without meaning that every noisy pixel in the image is marked down as an edge. Still, however, we have the problem of choosing appropriate thresholdingparameters, and suitable thresholding values may vary over the image.Some edge-detection operators are instead based upon second-order derivatives of the intensity. This essentially captures the rate of change in the intensity gradient. Thus, in the ideal continuous case, detection of zero-crossings in the second derivative captures local maxima in the gradient.We can come to a conclusion that,to be classified as a meaningful edge point,the transition in gray level associated with that point has to be significantly stronger than the background at that point.Since we are dealing with local computations,the method of choice to determine whether a value is “significant” or not id to use a threshold.Thus we define a point in an image as being as being an edge point if its two-dimensional first-order derivative is greater than a specified criterion of connectedness is by definition an edge.The term edge segment generally is used if the edge is short in relation to the dimensions of the image.A key problem in segmentation is to assemble edge segments into longer edges.An alternate definition if we elect to use the second-derivative is simply to define the edge ponits in an image as the zero crossings of its second derivative.The definition of an edge in this case is the same as above.It is important to note that these definitions do not guarantee success in finding edge in an image.They simply give us a formalism to look for them.First-order derivatives in an image are computed using the gradient.Second-order derivatives are obtained using the Laplacian.数字图像处理和边缘检测数字图像处理在数字图象处理方法的兴趣从两个主要应用领域的茎：改善人类解释图像信息;和用于存储，传输，和表示用于自主机器感知图像数据的处理。
数字图像处理英文原版及翻译
数字图象处理英文原版及翻译Digital Image Processing: English Original Version and TranslationIntroduction:Digital Image Processing is a field of study that focuses on the analysis and manipulation of digital images using computer algorithms. It involves various techniques and methods to enhance, modify, and extract information from images. In this document, we will provide an overview of the English original version and translation of digital image processing materials.English Original Version:The English original version of digital image processing is a comprehensive textbook written by Richard E. Woods and Rafael C. Gonzalez. It covers the fundamental concepts and principles of image processing, including image formation, image enhancement, image restoration, image segmentation, and image compression. The book also explores advanced topics such as image recognition, image understanding, and computer vision.The English original version consists of 14 chapters, each focusing on different aspects of digital image processing. It starts with an introduction to the field, explaining the basic concepts and terminology. The subsequent chapters delve into topics such as image transforms, image enhancement in the spatial domain, image enhancement in the frequency domain, image restoration, color image processing, and image compression.The book provides a theoretical foundation for digital image processing and is accompanied by numerous examples and illustrations to aid understanding. It also includes MATLAB codes and exercises to reinforce the concepts discussed in each chapter. The English original version is widely regarded as a comprehensive and authoritative reference in the field of digital image processing.Translation:The translation of the digital image processing textbook into another language is an essential task to make the knowledge and concepts accessible to a wider audience. The translation process involves converting the English original version into the target language while maintaining the accuracy and clarity of the content.To ensure a high-quality translation, it is crucial to select a professional translator with expertise in both the source language (English) and the target language. The translator should have a solid understanding of the subject matter and possess excellent language skills to convey the concepts accurately.During the translation process, the translator carefully reads and comprehends the English original version. They then analyze the text and identify any cultural or linguistic nuances that need to be considered while translating. The translator may consult subject matter experts or reference materials to ensure the accuracy of technical terms and concepts.The translation process involves several stages, including translation, editing, and proofreading. After the initial translation, the editor reviews the translated text to ensure its coherence, accuracy, and adherence to the target language's grammar and style. The proofreader then performs a final check to eliminate any errors or inconsistencies.It is important to note that the translation may require adapting certain examples, illustrations, or exercises to suit the target language and culture. This adaptation ensures that the translated version resonates with the local audience and facilitates better understanding of the concepts.Conclusion:Digital Image Processing: English Original Version and Translation provides a comprehensive overview of the field of digital image processing. The English original version, authored by Richard E. Woods and Rafael C. Gonzalez, serves as a valuable reference for understanding the fundamental concepts and techniques in image processing.The translation process plays a crucial role in making this knowledge accessible to non-English speakers. It involves careful selection of a professional translator, thoroughunderstanding of the subject matter, and meticulous translation, editing, and proofreading stages. The translated version aims to accurately convey the concepts while adapting to the target language and culture.By providing both the English original version and its translation, individuals from different linguistic backgrounds can benefit from the knowledge and advancements in digital image processing, fostering international collaboration and innovation in this field.。
图像处理专业英语词汇
图像处理专业英语词汇图像处理是计算机科学领域中的一个重要分支，它涉及到数字图像的获取、处理、分析和展示。
在图像处理领域，有许多专业的英语词汇需要掌握。
本文将介绍一些常用的图像处理专业英语词汇，帮助读者更好地理解和运用这些术语。
一、数字图像获取数字图像获取是指通过传感器或者扫描仪等设备获取图像的过程。
在这个过程中，有一些常用的英语词汇需要了解。
1. Sensor（传感器）- 一种用于检测和测量环境变化的装置，常用于捕捉图像中的光线信息。
2. Scanner（扫描仪）- 一种设备，用于将纸质图像或照片转换为数字图像。
3. Resolution（分辨率）- 衡量图像细节的能力，通常以像素为单位表示。
4. Pixel（像素）- 图像的最小单位，每个像素代表一个颜色值。
5. Color depth（颜色深度）- 表示每个像素可以显示的颜色数量，通常以位数表示。
二、图像处理基础图像处理的基础是对图像进行各种操作和处理，以改善图像质量或提取有用的信息。
以下是一些常用的英语词汇。
1. Enhancement（增强）- 通过调整图像的对比度、亮度或者颜色等参数来改善图像质量。
2. Filtering（滤波）- 通过应用滤波器来改变图像的频率特性或去除噪声。
3. Segmentation（分割）- 将图像分成不同的区域或对象，以便更好地进行分析和处理。
4. Edge detection（边缘检测）- 识别图像中的边缘或轮廓。
5. Histogram（直方图）- 表示图像中不同灰度级的像素数量的统计图。
三、图像分析与识别图像分析和识别是图像处理的重要应用之一，它涉及到从图像中提取和识别有用的信息。
以下是一些常用的英语词汇。
1. Feature extraction（特征提取）- 从图像中提取有用的特征，用于分类和识别。
2. Pattern recognition（模式识别）- 通过比较图像中的模式和已知的模式，来识别图像中的对象或场景。
图像处理专业英语词汇
图像处理专业英语词汇Introduction:As a professional in the field of image processing, it is important to have a strong command of the relevant technical terminology in English. This will enable effective communication with colleagues, clients, and stakeholders in the industry. In this document, we will provide a comprehensive list of commonly used English vocabulary related to image processing, along with their definitions and usage examples.1. Image Processing:Image processing refers to the manipulation and analysis of digital images using computer algorithms. It involves various techniques such as image enhancement, restoration, segmentation, and recognition.2. Pixel:A pixel, short for picture element, is the smallest unit of a digital image. It represents a single point in an image and contains information about its color and intensity.Example: The resolution of a digital camera is determined by the number of pixels it can capture in an image.3. Resolution:Resolution refers to the level of detail that can be captured or displayed in an image. It is typically measured in pixels per inch (PPI) or dots per inch (DPI).Example: Higher resolution images provide sharper and more detailed visuals.4. Image Enhancement:Image enhancement involves improving the quality of an image by adjusting its brightness, contrast, sharpness, and color balance.Example: The image processing software offers a range of tools for enhancing photographs.5. Image Restoration:Image restoration techniques are used to remove noise, blur, or other distortions from an image and restore it to its original quality.Example: The image restoration algorithm successfully eliminated the noise in the scanned document.6. Image Segmentation:Image segmentation is the process of dividing an image into multiple regions or objects based on their characteristics, such as color, texture, or intensity.Example: The image segmentation algorithm accurately separated the foreground and background objects.7. Image Recognition:Image recognition involves identifying and classifying objects or patterns in an image using machine learning and computer vision techniques.Example: The image recognition system can accurately recognize and classify different species of flowers.8. Histogram:A histogram is a graphical representation of the distribution of pixel intensities in an image. It shows the frequency of occurrence of different intensity levels.Example: The histogram analysis revealed a high concentration of dark pixels in the image.9. Edge Detection:Edge detection is a technique used to identify and highlight the boundaries between different objects or regions in an image.Example: The edge detection algorithm accurately detected the edges of the objects in the image.10. Image Compression:Image compression is the process of reducing the file size of an image without significant loss of quality. It is achieved by removing redundant or irrelevant information from the image.Example: The image compression algorithm reduced the file size by 50% without noticeable loss of image quality.11. Morphological Operations:Morphological operations are a set of image processing techniques used to analyze and manipulate the shape and structure of objects in an image.Example: The morphological operations successfully removed small noise particles from the image.12. Feature Extraction:Feature extraction involves identifying and extracting relevant features or characteristics from an image for further analysis or classification.Example: The feature extraction algorithm extracted texture features from the image for cancer detection.Conclusion:This comprehensive list of English vocabulary related to image processing provides a solid foundation for effective communication in the field. By familiarizing yourself with these terms and their usage, you will be better equipped to collaborate, discuss, andpresent ideas in the context of image processing. Remember to continuously update your knowledge as the field evolves and new techniques emerge.。
图像处理_AmsterdamLi...
Amsterdam Library of Object Images(美国弗吉尼亚大学物体图像数据库)数据摘要：ALOI is a color image collection of one-thousand small objects, recorded for scientific purposes. In order to capture the sensory variation in object recordings, we systematically varied viewing angle, illumination angle, and illumination color for each object, and additionally captured wide-baseline stereo images. We recorded over a hundred images of each object, yielding a total of 110,250 images for the collection. See Technical Details for a description of the acquisition setup.中文关键词：目标识别,视角,光照角度,光照颜色,立体图像,英文关键词：object recognition viewing angle,illumination angle,illumination color,-baseline stereo images,数据格式：IMAGE数据用途：To use object recognition数据详细介绍：Amsterdam Library of Object Images (ALOI)ALOI is a color image collection of one-thousand small objects, recorded for scientific purposes. In order to capture the sensory variation in object recordings, we systematically varied viewing angle, illumination angle, and illumination color for each object, and additionally captured wide-baseline stereo images. We recorded over a hundred images of each object, yielding a total of 110,250 images for the collection. See Technical Details for a description of the acquisition setup.Details have been published in: J. M. Geusebroek, G. J. Burghouts, and A. W. M. Smeulders, The Amsterdam library of object images, Int. J. Comput. Vision, 61(1), 103-112, January, 2005Please consult the paper for all details on recording of the collection.Each object was recorded with only one out of five lights turned on, yielding five different illumination angles (conditions l1-l5). By switching the camera, and turning the stage towards that camera, the illumination bow is virtually turned by 15 (camera c2) and 30 degrees (camera c3), respectively. Hence, the aspect of the objects viewed by each camera is identical, but light direction has shifted by 15 and 30 degrees in azimuth. In total, this results in 15 different illumination angles.Furthermore, combinations of lights were used to illuminate the object. Turning on two lights at the sides of the object yielded an oblique illumination from right (condition l6) and left (condition l7). Turning on all lights (condition l8) yields a sort of hemispherical illumination, although restricted to a more narrow illumination sector than true hemisphere. In this way, a total of 24 different illumination conditions were generated, conditions c[1..3]l[1..8].Each object was recorded in frontal view, with all five lamps turned on. Illumination color temperature is changed from 2175K to 3075K. Cameras were white balanced at 3075K, resulting in objects illuminated under a reddish to white illumination color, conditions i110, i120, ..., i250.The frontal camera was used to record 72 aspects of the objects by rotating the object in the plane at 5 degree resolution, conditions r0..r355. This collection is similar to the COIL collection.The frontal camera was used to record 72 aspects of the objects by rotating the object in the plane at 5 degree resolution, conditions r0..r355. This collection is similar to the COIL collection.数据预览：点此下载完整数据集。
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天津大学《图像处理》课程教学大纲
课程代码：2160063 课程名称：图像处理
学 时： 32 学 分： 2
学时分配： 授课：24 上机： 实验：8 实践： 实践（周）：
授课学院： 计算机学院 更新时间： 2011.6
适用专业： 计算机科学系
先修课程： 数字信号处理、程序设计
一．课程的性质与目的
《图像处理》是计算机科学系的一门专业必选课。
本课程主要介绍数字图像处理的一些基本概念、图像基础知识、图像增强和图像分割等基本内容。
二．教学基本要求
通过本课程学习，使学生了解数字图像处理的特点，掌握基本的图像处理方法，为后续模式识别等课程以及进一步学习打下理论基础。
三．教学内容
1、图像基础知识
（1）．了解数字图像处理的特点和主要内容。
（2）．了解数字图像处理的应用与发展。
（3）．理解视觉模型和人眼的视觉特性。
（4）．了解一些基本的数字图像的概念。
2、图像空域增强
（1）．掌握灰度级线性变换、分段线性变换等图像对比度增强的方法。
（2）．掌握直方图均衡化方法、中值滤波和加权平均等平滑算法。
（3）．理解图像频域增强方法以及其与时域增强方法的联系与区别, 理解图像锐化的意义，掌握Robert梯度法，了解同态滤波等技术。
3、图象分割
（1）．理解图象分割技术。
（2）．理解图像的目标表达和描述的基本概念
4.数学形态学
（1）.理解并掌握基本形态学运算。
（2）.利用形态学运算能够实现图像增强、图像分析等操作。
5.附录-彩色图像基本知识
(1).了解彩色图像的表示及颜色空间。
(2).了解彩色图像的处理方法。
四．学时分配
教学内容 授课 上机 实验 实践 实践(周)图像基础知识 2
图像增强 8 3
图像分割 8 3
数学形态学 4 2
彩色图像的基本知识 2
总计： 24 8
五．评价与考核方式
实验：80%
平时成绩：20%
六．教材与主要参考资料
教材：
《图像工程（上册）：图像处理和分析》，章毓晋编著，北京：清华大学出版社，1999, 3.
主要参考资料：
1，《数字图像处理（第二版）》，冈萨雷斯编著，电子工业出版社，2003,3
2，数字图像处理基础，朱虹，科学出版社，2005,10
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批准日期：年月日
TU Syllabus for Image Processing
Code:
2160063 Title: Image Processing Semester Hours:
24
Credits:
2
Semester Hour
Structure Lecture ：24 Computer Lab ： Experiment ：8 Practice ：
Practice (Week)： Offered by: School of CS
Date:
2011.6
for: Department of Computer Science Prerequisite: Digital signal processing, Programming
1. Objective
“Image Processing ” is a selective course of specialized subject of department of computer science in school of CS. The main contents of this course include the basic concepts of digital image, fundamentals, image enhancement and image segmentation etc.
2. Course Description
The students should know about the characteristics of digital image processing and comprehend the basic image processing methods, which can be used in further study, such as pattern recognition.
3. Topics
1、Fundamentals in digital image
（1）．Know about the main contents and characteristics of image processing. （2）．Know about the applications and development of image processing. （3）．Comprehend vision model and human vision characteristics. （4）．Know about some basic concepts of digital image. 2、Image enhancement in spacial domain
（1）．Comprehend the basic image contrast enhancement method.
（2）．Comprehend the basic image smoothing method, such as median filter, KNN, etc.
（3）．Comprehend the image enhancement methods in frequency domain
3、Image segmentation
（1）．Comprehend and realize the basic image segmentation technologies. （2）．Comprehend the target representation and description.
4、Mathematical Morphology.
（1）. Comprehends and be able to use fundamental operation of mathematical morphology.
（2）. Be able to realize image enhancement and image analysis using different operations of mathematical morphology.
5. Appendix
(1).know about color image representation and the color space.
(2).know about the basic color image processing methods.
4. Semester Hour Structure
Topics Lecture Computer
Lab.
Experiment Practice
Practice
(Week)
Digital image fundamentals 2
Image enhancement 8 3
Image segmentation 8 3
Mathematical morphology 4 2
color image fundamentals 2
Sum: 24 8
5. Grading
Experiment: 80%
Attendance: 20%
6. Text-Book & Additional Readings
Text-book:
Image Engineering（1st volume）：image processing and analysis，Zhang Yujin，Tsinghua University Press，1999, 3.
Additional Readings：
1，Digital image processing（2nd edition），Gonzalez，Electric Industry Press,2003,3 2，Digital image processing fundamental，Zhu Hong，China Science Press，2005.10
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