图像处理英文翻译
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。
dip国际术语
dip国际术语DIP国际术语：数字图像处理的基本概念数字图像处理（Digital Image Processing，简称DIP）是指对数字图像进行各种处理、分析和操作的技术和方法。
在DIP领域中，有许多术语被广泛使用，这些术语涵盖了图像获取、图像增强、图像分割、图像压缩等多个方面。
本文将介绍一些DIP国际术语的基本概念和应用。
1. 图像获取（Image Acquisition）图像获取是指使用传感器或其他设备将现实世界中的光学信息转换为数字图像的过程。
常见的图像获取设备包括数码相机、扫描仪和医学影像设备等。
图像获取的质量对后续的图像处理结果有重要影响，因此需要合理选择设备、控制光照条件和调整参数。
2. 图像增强（Image Enhancement）图像增强是指通过调整图像的亮度、对比度、颜色等属性，使图像在视觉上更加清晰、鲜艳或易于分析的过程。
常用的图像增强方法包括直方图均衡化、滤波和锐化等。
图像增强可以改善图像的观感效果，提高图像的质量和清晰度。
3. 图像分割（Image Segmentation）图像分割是将图像划分为不同区域或对象的过程，其目标是提取出感兴趣的图像区域，为后续的图像分析和理解提供基础。
常见的图像分割方法包括阈值分割、边缘检测和区域生长等。
图像分割在医学影像、计算机视觉和目标检测等领域具有广泛应用。
4. 图像压缩（Image Compression）图像压缩是指通过减少图像数据的存储空间或传输带宽，以实现图像数据压缩和恢复的过程。
图像压缩可以分为有损压缩和无损压缩两种。
有损压缩在减小图像文件大小的同时会引入一定的信息损失，而无损压缩则可以完全恢复原始图像。
JPEG和PNG是常用的图像压缩格式。
5. 形态学处理（Morphological Processing）形态学处理是一种基于图像形状和结构的图像处理方法，主要用于图像的特征提取和形态学运算。
形态学处理主要包括腐蚀、膨胀、开运算和闭运算等操作。
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 file extensions, such as the jpg file extension used in the JPEG (Joint Photographic Experts Group) image compression standard.Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape. The material in this chapter begins a transition from processes that output images to processes that output image attributes.Segmentation procedures partition an image into its constituent parts or objects. In general, autonomous segmentation is one of the most difficult tasks indigital image processing. A rugged segmentation procedure brings the process a long way toward successful solution of imaging problems that require objects to be identified individually. On the other hand, weak or erratic segmentation algorithms almost always guarantee eventual failure. In general, the more accurate the segmentation, the more likely recognition is to succeed.Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the 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。
CV专业名词中英文对照
Common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相关性correlation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内插interpolation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 problemAAberration 像差Accessory 附件Accessory Shoes 附件插座、热靴Achromatic 消色差的Active 主动的、有源的Acutance 锐度Acute-matte 磨砂毛玻璃Adapter 适配器Advance system 输片系统AE Lock(AEL) 自动曝光锁定AF(Autofocus) 自动聚焦AF Illuminator AF照明器AF spotbeam projector AF照明器Alkaline 碱性Ambient light 环境光Amplification factor 放大倍率Angle finder 弯角取景器Angle of view 视角Anti-Red-eye 防红眼Aperture 光圈Aperture priority 光圈优先APO(APOchromat) 复消色差APZ(Advanced Program zoom) 高级程序变焦Arc 弧形ASA(American Standards Association) 美国标准协会Astigmatism 像散Auto bracket 自动包围Auto composition 自动构图Auto exposure 自动曝光Auto exposure bracketing 自动包围曝光Auto film advance 自动进片Auto flash 自动闪光Auto loading 自动装片Auto multi-program 自动多程序Auto rewind 自动退片Auto wind 自动卷片Auto zoom 自动变焦Automatic exposure(AE) 自动曝光Automation 自动化Auxiliary 辅助BBack 机背Back light 逆光、背光Back light compensation 逆光补偿Background 背景Balance contrast 反差平衡Bar code system 条形码系统Barrel distortion 桶形畸变BAse-Stored Image Sensor (BASIS) 基存储影像传感器Battery check 电池检测Battery holder 电池手柄Bayonet 卡口Bellows 皮腔Blue filter 蓝色滤光镜Body-integral 机身一体化Bridge camera 桥梁相机Brightness control 亮度控制Built in 内置Bulb B 门Button 按钮CCable release 快门线Camera 照相机Camera shake 相机抖动Cap 盖子Caption 贺辞、祝辞、字幕Card 卡Cartridges 暗盒Case 机套CCD(Charge Coupled Device) 电荷耦合器件CdS cell 硫化镉元件Center spot 中空滤光镜Center weighted averaging 中央重点加权平均Chromatic Aberration 色差Circle of confusion 弥散圆Close-up 近摄Coated 镀膜Compact camera 袖珍相机Composition 构图Compound lens 复合透镜Computer 计算机Contact 触点Continuous advance 连续进片Continuous autofocus 连续自动聚焦Contrast 反差、对比Convetor 转换器Coreless 无线圈Correction 校正Coupler 耦合器Coverage 覆盖范围CPU(Central Processing Unit) 中央处理器Creative expansion card 艺术创作软件卡Cross 交叉Curtain 帘幕Customized function 用户自选功能DData back 数据机背Data panel 数据面板Dedicated flash 专用闪光灯Definition 清晰度Delay 延迟、延时Depth of field 景深Depth of field preview 景深预测Detection 检测Diaphragm 光阑Diffuse 柔光Diffusers 柔光镜DIN (Deutsche Industrische Normen) 德国工业标准Diopter 屈光度Dispersion 色散Display 显示Distortion 畸变Double exposure 双重曝光Double ring zoom 双环式变焦镜头Dreams filter 梦幻滤光镜Drive mode 驱动方式Duration of flash 闪光持续时间DX-code DX编码EED(Extra low Dispersion) 超低色散Electro selective pattern(ESP) 电子选择模式EOS(Electronic Optical System) 电子光学系统Ergonomic 人体工程学EV(Exposure value) 曝光值Evaluative metering 综合评价测光Expert 专家、专业Exposure 曝光Exposure adjustment 曝光调整Exposure compensation 曝光补偿Exposure memory 曝光记忆Exposure mode 曝光方式Exposure value(EV) 曝光值Extension tube 近摄接圈Extension ring 近摄接圈External metering 外测光Extra wide angle lens 超广角镜头Eye-level fixed 眼平固定Eye-start 眼启动Eyepiece 目镜Eyesight correction lenses 视力校正镜FField curvature 像场弯曲Fill in 填充(式)Film 胶卷(片)Film speed 胶卷感光度Film transport 输片、过片Filter 滤光镜Finder 取景器First curtain 前帘、第一帘幕Fish eye lens 鱼眼镜头Flare 耀斑、眩光Flash 闪光灯、闪光Flash range 闪光范围Flash ready 闪光灯充电完毕Flexible program 柔性程序Focal length 焦距Focal plane 焦点平面Focus 焦点Focus area 聚焦区域Focus hold 焦点锁定Focus lock 焦点锁定Focus prediction 焦点预测Focus priority 焦点优先Focus screen 聚焦屏Focus tracking 焦点跟踪Focusing 聚焦、对焦、调焦Focusing stages 聚焦级数Fog filter 雾化滤光镜Foreground 前景Frame 张数、帧Freeze 冻结、凝固Fresnel lens 菲涅尔透镜、环状透镜Frontground 前景Fuzzy logic 模糊逻辑GGlare 眩光GN(Guide Number) 闪光指数GPD(Gallium Photo Diode) 稼光电二极管Graduated 渐变HHalf frame 半幅Halfway 半程Hand grip 手柄High eye point 远视点、高眼点High key 高调Highlight 高光、高亮Highlight control 高光控制High speed 高速Honeycomb metering 蜂巢式测光Horizontal 水平Hot shoe 热靴、附件插座Hybrid camera 混合相机Hyper manual 超手动Hyper program 超程序Hyperfocal 超焦距IIC(Integrated Circuit) 集成电路Illumination angle 照明角度Illuminator 照明器Image control 影像控制Image size lock 影像放大倍率锁定Infinity 无限远、无穷远Infra-red(IR) 红外线Instant return 瞬回式Integrated 集成Intelligence 智能化Intelligent power zoom 智能化电动变焦Interactive function 交互式功能Interchangeable 可更换Internal focusing 内调焦Interval shooting 间隔拍摄ISO(International Standard Association) 国际标准化组织JJIS(Japanese Industrial Standards)日本工业标准LLandscape 风景Latitude 宽容度LCD data panel LCD数据面板LCD(Liquid Crystal Display) 液晶显示LED(Light Emitting Diode) 发光二极管Lens 镜头、透镜Lens cap 镜头盖Lens hood 镜头遮光罩Lens release 镜头释放钮Lithium battery 锂电池Lock 闭锁、锁定Low key 低调Low light 低亮度、低光LSI(Large Scale Integrated) 大规模集成MMacro 微距、巨像Magnification 放大倍率Main switch 主开关Manual 手动Manual exposure 手动曝光Manual focusing 手动聚焦Matrix metering 矩阵式测光Maximum 最大Metered manual 测光手动Metering 测光Micro prism 微棱Minimum 最小Mirage 倒影镜Mirror 反光镜Mirror box 反光镜箱Mirror lens 折反射镜头Module 模块Monitor 监视、监视器Monopod 独脚架Motor 电动机、马达Mount 卡口MTF (Modulation Transfer Function 调制传递函数Multi beam 多束Multi control 多重控制Multi-dimensional 多维Multi-exposure 多重曝光Multi-image 多重影Multi-mode 多模式Multi-pattern 多区、多分区、多模式Multi-program 多程序Multi sensor 多传感器、多感光元件Multi spot metering 多点测光Multi task 多任务NNegative 负片Neutral 中性Neutral density filter 中灰密度滤光镜Ni-Cd battery 镍铬(可充电)电池OOff camera 离机Off center 偏离中心OTF(Off The Film) 偏离胶卷平面One ring zoom 单环式变焦镜头One touch 单环式Orange filter 橙色滤光镜Over exposure 曝光过度PPanning 摇拍Panorama 全景Parallel 平行Parallax 平行视差Partial metering 局部测光Passive 被动的、无源的Pastels filter 水粉滤光镜PC(Perspective Control) 透视控制Pentaprism 五棱镜Perspective 透视的Phase detection 相位检测Photography 摄影Pincushion distortion 枕形畸变Plane of focus 焦点平面Point of view 视点Polarizing 偏振、偏光Polarizer 偏振镜Portrait 人像、肖像Power 电源、功率、电动Power focus 电动聚焦Power zoom 电动变焦Predictive 预测Predictive focus control 预测焦点控制Preflash 预闪Professional 专业的Program 程序Program back 程序机背Program flash 程序闪光Program reset 程序复位Program shift 程序偏移Programmed Image Control (PIC) 程序化影像控制QQuartz data back 石英数据机背RRainbows filter 彩虹滤光镜Range finder 测距取景器Release priority 释放优先Rear curtain 后帘Reciprocity failure 倒易律失效Reciprocity Law 倒易律Recompose 重新构图Red eye 红眼Red eye reduction 红眼减少Reflector 反射器、反光板Reflex 反光Remote control terminal 快门线插孔Remote cord 遥控线、快门线Resolution 分辨率Reversal films 反转胶片Rewind 退卷Ring flash 环形闪光灯ROM(Read Only Memory) 只读存储器Rotating zoom 旋转式变焦镜头RTF(Retractable TTL Flash) 可收缩TTL闪光灯SSecond curtain 后帘、第二帘幕Secondary Imaged Registration(SIR) 辅助影像重合Segment 段、区Selection 选择Self-timer 自拍机Sensitivity 灵敏度Sensitivity range 灵敏度范围Sensor 传感器Separator lens 分离镜片Sepia filter 褐色滤光镜Sequence zoom shooting 顺序变焦拍摄Sequential shoot 顺序拍摄Servo autofocus 伺服自动聚焦Setting 设置Shadow 阴影、暗位Shadow control 阴影控制Sharpness 清晰度Shift 偏移、移动Shutter 快门Shutter curtain 快门帘幕Shutter priority 快门优先Shutter release 快门释放Shutter speed 快门速度Shutter speed priority 快门速度优先Silhouette 剪影Single frame advance 单张进片Single shot autofocus 单次自动聚焦Skylight filter 天光滤光镜Slide film 幻灯胶片Slow speed synchronization 慢速同步SLD(Super Lower Dispersion) 超低色散SLR(Single Lens Reflex) 单镜头反光照相机SMC(Super Multi Coated) 超级多层镀膜Soft focus 柔焦、柔光SP(Super Performance) 超级性能SPC(Silicon Photo Cell) 硅光电池SPD(Silicon Photo Dioxide) 硅光电二极管Speedlight 闪光灯、闪光管Split image 裂像Sport 体育、运动Spot metering 点测光Standard 标准Standard lens 标准镜头Starburst 星光镜Stop 档Synchronization 同步TTele converter 增距镜、望远变换器Telephoto lens 长焦距镜头Trailing-shutter curtain 后帘同步Trap focus 陷阱聚焦Tripod 三脚架TS(Tilt and Shift) 倾斜及偏移TTL flash TTL闪光TTL flash metering TTL闪光测光TTL(Through The Lens) 通过镜头、镜后Two touch 双环UUD(Ultra-low Dispersion) 超低色散Ultra wide 超阔、超广Ultrasonic 超声波UV(Ultra-Violet) 紫外线Under exposure 曝光不足VVari-colour 变色Var-program 变程序Variable speed 变速Vertical 垂直Vertical traverse 纵走式View finder 取景器WWarm tone 暖色调Wide angle lens 广角镜头Wide view 广角预视、宽区预视Wildlife 野生动物Wireless remote 无线遥控World time 世界时间XX-sync X-同步ZZoom 变焦Zoom lens 变焦镜头Zoom clip 变焦剪裁Zoom effect 变焦效果OtherTTL 镜后测光NTTL 非镜后测光UM 无机内测光，手动测光MM 机内测光，但需手动设定AP 光圈优先SP 快门优先PR 程序暴光ANCILLARY DEVICES 辅助产品BACKPLANES 底板CABLES AND CONNECTORS 连线及连接器ENCLOSURES 围圈FACTORY AUTOMATION 工厂自动化POWER SUPPLIES 电源APPLICATION-SPECIFIC SOFTWARE 应用软件INDUSTRIAL-INSPECTION SOFTWARE 工业检测软件MEDICAL-IMAGING SOFTWARE 医药图象软件SCIENTIFIC-ANALYSIS SOFTWARE 科学分析软件SEMICONDUCTOR-INSPECTION SOFTWARE 半导体检测软件CAMERAS 相机AREA-ARRAY CAMERAS 面阵相机CAMERA LINK CAMERAS CAMERA-LINK相机CCD CAMERAS-COLOR ccd彩色相机CCD CAMERAS COOLED ccoled型ccd相机CHARGE-INJECTION-DEVICE CAMERAS 充电相机CMOS CAMERAS cmos相机DIGITAL-OUTPUT CAMERAS 数码相机FIREWIRE(1394) CAMERAS 1394接口相机HIGH-SPEED VIDEO CAMERAS 高速摄象机INFRARED CAMERAS 红外相机LINESCAN CAMERAS 行扫描相机LOW-LIGHT-LEVEL CAMERAS 暗光相机MULTISPECTRAL CAMERAS 多光谱相机SMART CAMERAS 微型相机TIME-DELAY-AND-INTEGRATION CAMERAS 时间延迟集成相机USB CAMERAS usb接口相机VIDEO CAMERAS 摄象机DIGITIZERS 数字转换器MEASUREMENT DIGITIZERS 数字测量器MOTION-CAPTURE DIGITIZERS 数字运动捕捉器DISPLAYS 显示器CATHODE-RAY TUBES(CRTs) 阴极摄像管INDUSTRIAL DISPLAYS 工业用型显示器LIQUID-CRYSTAL DISPLAYS 液晶显示器ILLUMINATION SYSTEMS 光源系统BACKLIGHTING DEVICES 背光源FIBEROPTIC ILLUMINATION SYSTEMS 光纤照明系统FLUORESCENT ILLUMINATION SYSTEMS荧光照明系统INFRARED LIGHTING 红外照明LED LIGHTING led照明STRUCTURED LIGHTING 结构化照明ULTRAVIOLET ILLUMINATION SYSTEMS 紫外照明系统WHITE-LIGHT ILLUMINATION SYSTEMS 白光照明系统XENON ILLUMINATION SYSTEMS 氙气照明系统IMAGE-PROCESSING SYSTEMS 图象处理系统AUTOMATION/ROBOTICS 自动化/机器人技术DIGITAL IMAGING SYSTEMS 数字图象系统DOCUMENT-IMAGING SYSTEMS 数据图象系统GUIDANCE/TRACKING SYSTEMS 制导/跟踪系统INFRARED IMAGING SYSTEMS 红外图象系统INSPECTION/NONDESTRUCTIVE TESTING SYSTEMS 检测/非破坏性测试系统INSTRUMENTATION SYSTEMS 测试设备系统INTELLIGENT TRANSPORTATION SYSTEMS 智能交通系统MEDICAL DIAGNOSTICS SYSTEMS 医疗诊断系统METROLOGY/MEASUREMENT/GAUGING SYSTEMS 测绘系统MICROSCOPY SYSTEMS 微观系统MOTION-ANALYSIS SYSTEMS 运动分析系统OPTICAL-CHARACTER-RECOGNITION/OPTICAL-CHARACTER-VERIFICATION SYSTEMS 光学文字识别系统PROCESS-CONTROL SYSTEMS 处理控制系统QUALITY-ASSURANCE SYSTEMS 高保真系统REMOTE SENSING SYSTEMS 遥感系统WEB-SCANNING SYSTEMS 网状扫描系统IMAGE-PROCESSING TOOLKITS 图象处理工具包COMPILERS 编译器DATA-ACQUISITION TOOLKITS 数据采集工具套件DEVELOPMENT TOOLS 开发工具DIGITAL-SIGNAL-PROCESSOR(DSP) DEVELOPMENT TOOLKITS 数字信号处理开发工具套件REAL-TIME OPERATING SYSTEMS(RTOSs) 实时操作系统WINDOWS 窗口IMAGE SOURCES 图象资源FLASHLAMPS 闪光灯FLUORESCENT SOURCES 荧光源LASERS 激光器LIGHT-EMITTING DIODES(LEDs) 发光二极管STROBE ILLUMINATION 闪光照明TUNGSTEN LAMPS 钨灯ULTRAVIOLET LAMPS 紫外灯WHITE-LIGHT SOURCES 白光灯XENON LAMPS 氙气灯X-RAY SOURCES x射线源IMAGE-STORAGE DEVICES 图象存储器HARD DRIVES 硬盘设备OPTICAL STORAGE DEVICES 光存储设备RAID STORAGE DEVICES RAID存储设备（廉价磁盘冗余阵列设备）INTEGRATED CIRCUITS 综合电路ASICS 专用集成电路ANALOG-TO-DIGITAL CONVERTERS 模数转换器COMMUNICATIONS CONTROLLERS 通信控制器DIGITAL-SIGNAL PROCESSORS 数字信号处理器DIGITAL-TO-ANALOG CONVERTERS 数模转换器DISPLAY CONROLLERS 显示器控制器FIELD-PROGRAMMABLE GATE 现场可编程门阵列ARRAYS 阵列GRAPHICS-DISPLAY CONTROLLERS 图形显示控制器IMAGE-PROCESSING ICs 图象处理芯片MIXED-SIGNAL ICs 混合信号芯片VIDEO-PROCESSING ICs 视频处理芯片LENSES 镜头CAMERA LENSES 相机镜头ENLARGING LENSES 放大镜HIGH-RESOLUTION LENSES 高分辨率镜头IMAGE-SCANNING LENSES 图象扫描镜头PROJECTION LENSES 聚光透镜TELECENTRIC LENSES 望远镜VIDEO LENSES 摄象机镜头MONITORS 显示器CATHODE-RAY-TUBE(CRT) MONITORS, COLOR crt彩色监视器CATHODE-RAY-TUBE(CRT) MONITORS, MONOCHROME 单色crt监视器LIQUID-CRYSTAL-DISPLAY(LED) MONITORS lcd监视器。
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 常见英文名词解释
Algebraic operation 代数运算；一种图像处理运算，包括两幅图像对应像素的和、差、积、商。
Aliasing 走样（混叠）；当图像像素间距和图像细节相比太大时产生的一种人工痕迹。
Arc 弧；图的一部分；表示一曲线一段的相连的像素集合。
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。
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（比较：外部像素、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从而检测出其中物体差别的技术。
Class 类；见模或类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部（反义词为凸）Connected 连通的Contour 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Convex 凸的；物体是凸的是指连接物体内部任意两点的直线均落在物体内部。
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。
matlab图像处理外文翻译外文文献
matlab图像处理外文翻译外文文献附录A 英文原文Scene recognition for mine rescue robotlocalization based on visionCUI Yi-an(崔益安), CAI Zi-xing(蔡自兴), WANG Lu(王璐)Abstract：A new scene recognition system was presented based on fuzzy logic and hidden Markov model(HMM) that can be applied in mine rescue robot localization during emergencies. The system uses monocular camera to acquire omni-directional images of the mine environment where the robot locates. By adopting center-surround difference method, the salient local image regions are extracted from the images as natural landmarks. These landmarks are organized by using HMM to represent the scene where the robot is, and fuzzy logic strategy is used to match the scene and landmark. By this way, the localization problem, which is the scene recognition problem in the system, can be converted into the evaluation problem of HMM. The contributions of these skills make the system have the ability to deal with changes in scale, 2D rotation and viewpoint. The results of experiments also prove that the system has higher ratio of recognition and localization in both static and dynamic mine environments.Key words: robot location; scene recognition; salient image; matching strategy; fuzzy logic; hidden Markov model1 IntroductionSearch and rescue in disaster area in the domain of robot is a burgeoning and challenging subject[1]. Mine rescue robot was developed to enter mines during emergencies to locate possible escape routes for those trapped inside and determine whether it is safe for human to enter or not. Localization is a fundamental problem in this field. Localization methods based on camera can be mainly classified into geometric, topological or hybrid ones[2]. With its feasibility and effectiveness, scene recognition becomes one of the important technologies of topological localization.Currently most scene recognition methods are based on global image features and have two distinct stages: training offline and matching online.。
photoshop常用英语单词——中英文对照
photoshop常用英语单词——中英文对照photoshop 常用英语单词——中英文对照作者：胡建鹏关键词：常用英语单词添加时间：2008-3-12 17:15:15Aaccent 加重accurate 精确acquire 获得action 操作，运算adjust 调整ambient light 环境光angle 角度anti-aliased 平滑处理arbitrary 任意的arrange 排列arrow 箭头artistic 艺术的，美术的--------------------------------------------------------------------------------Bbalance 平衡bar 条，栏base 基准batch 批量，成批bevel 使成斜角，斜切bilinear 双线性插值bitmap 位图，点阵图blend 混合，调和blur 模糊bold 加粗border 边界，边框brightness 亮度brush 画笔，笔形build 建造，创立burn 焦化，烧黑button 按钮--------------------------------------------------------------------------------Ccache 快速存储器calculation 计算calibrate 校准，校验canvas 画布carve 镌刻cascade 层叠chalk 粉笔，作记号channel 通道，频道charcoal 炭笔画chrome 铬黄，铬合金classic 经典的click （单）击（鼠标）clipboard 剪贴版clone 复制cloud 云彩command 命令content 内容continue 连续的，持续的contour 轮廓线，周线contract 收缩，缩小contrast 反差，对比coordinate 协调，协作craquelue 裂纹crayon 蜡笔crop 剪裁crystallize 水晶curl 螺旋状物，卷曲的current 当前的cursor 光标，游标curve 曲线custom 自定义--------------------------------------------------------------------------------Ddarken 较黑的，使变黑daubs 涂抹define 定义design 设计destination 目标，对象difference 区别，差异diffuse 散乱扩散displace 转移distort 扭曲document 文档，文件dodge 加亮drop shadow 投影duotone 双色调duplicate 复制dust 灰尘，尘土--------------------------------------------------------------------------------Eedge 边界effect 效果作用embed 嵌入emboss 浮雕enlarge 放大equalize 平均化expand 扩充，扩展export 输出extrude 突出eyedropper 吸管--------------------------------------------------------------------------------Ffacet 刻面feather 晕开，羽化fill 填充flare 张开，闪耀flatten 变平flip 翻转fragment 碎片fresco 壁画--------------------------------------------------------------------------------Ggamut 整个领域general 普通的glass 玻璃杯glow 发光gradient 渐变grain 纹理graphics 图形grayscale 灰阶，灰度图grid 坐标格子group 群，组grow 成长guide 参考，参考线--------------------------------------------------------------------------------Hhalftone 半色调histogram 柱状图horizontal 水平hue 色相--------------------------------------------------------------------------------Iicon 图标image 图像import 输入index 索引Inner 内部的input 输入intensity 亮度inverse 相反invert 反转，反相--------------------------------------------------------------------------------Kkeyboard 键盘kilometre 千米，公里--------------------------------------------------------------------------------Llens 凹凸透镜，焦距level 级别，色阶light 光线location 定位--------------------------------------------------------------------------------Mmagic 有魔法的magic wand 魔棒mask 遮罩，掩膜maximum 最大值memory 记忆，内存merge 合并，融合minimum 最小值mode 模式，方式modify 修改，改变monitor 显示器，监视器monochrome 单色的mosaic 马塞克motion 运动multichannel 多通道模式--------------------------------------------------------------------------------Nnegative 负片，负像noise 噪音，糙点numeric 数字化的；分数--------------------------------------------------------------------------------Ooffset 位移，偏移option 选项ornament 装饰outline 轮廓，外形--------------------------------------------------------------------------------Ppalette 调色板parameter 参数，参变量paste 粘贴pastel 彩色粉笔，蜡笔画patchwork 补教，式样path 路径pattern 图案perspective 透视pinch 极化，凹陷或突起plug-ins 插件（滤镜）pointillize 乱点描述polar coordinates 极坐标poster 招贴画posterize 色调分离preference 偏好设定preview 预览previous 前一个pseudo 假的purge 消除--------------------------------------------------------------------------------Rradial 圆形range 范围，领域relief 救济，援救render 渲染，粉刷，上色reticulation 网状物revert 还原，复原ripple 涟漪，波纹rotate 旋转rough 粗略ruler 标尺--------------------------------------------------------------------------------Ssaturation 色彩饱和度scale 缩放scratch 暂时，临时screen 屏幕seam 缝，接缝selection 选择，工作区selective 精心挑选的separation 分离，分开sharpen 锐化shear 扭曲变形similar 相似的sketch 草图，画稿skew 偏斜的smart 灵活的，精巧的smooth 平滑的smudge 玷污，污点snapshot 快照solarize 中途曝光spatter 溅，洒落sponge 海绵spray 喷，喷涂stain 着色，染色stamp 盖章，盖印status 状态，状况stereo 立体声stroke 笔划，打击swirl 漩涡，卷状物--------------------------------------------------------------------------------Ttexture 质地，纹理threshold 两阶调化tile 平铺，窗口并联trace 跟踪，镂边trail 痕迹，足迹transform 转变，变形transparency 透明度twirl 卷曲，快速旋转--------------------------------------------------------------------------------Uultimate 最终的uncommon 罕见，非凡的units 单位，单元--------------------------------------------------------------------------------Vvertex 顶点，最高点vertical 垂直的video 视频，电视view 视图，显示方式virus 病毒visible 可见的，明显的visual 视觉的vogue 流行，时尚volume （磁盘）容量vortex 漩涡--------------------------------------------------------------------------------Wwatercolor 水彩watermark 水印wave 波纹，波动welder 焊接workstation 工作站wrinkle 皱纹--------------------------------------------------------------------------------Zzero 零zigzag 锯齿zoom in 放大zoom out 缩小一、File<文件>1.New<新建>2.Open<打开>3.Open As<打开为>4.Open Recent<最近打开文件>5.Close<关闭>6.Save<存储>7.Save As<存储为>8.Save for Web<存储为Web所用格式>9.Revert<恢复>10.Place<置入>11.Import<输入><1>PDF Image<2>Annotations<注释>12.Export<输出>13.Manage Workflow<管理工作流程><1>Check In<登记><2>Undo Check Out<还原注销><3>Upload To Server<上载到服务器><4>Add To Workflow<添加到工作流程><5>Open From Workflow<从工作流程打开> 14.Automate<自动><1>Batch<批处理><2>Create Droplet<创建快捷批处理><3>Conditional Mode Change<条件模式更改><4>Contact Sheet<联系表><5>Fix Image<限制图像><6>Multi<7>Picture package<图片包><8>Web Photo Gallery15.File Info<文件简介>16.Print Options<打印选项>17.Page Setup<页面设置>18.Print<打印>19.Jump to<跳转到>20.Exit<退出>二、Edit<编辑>1.Undo<还原>2.Step Forward<向前>3.Step Backward<返回>4.Fade<消退>5.Cut<剪切>6.Copy<拷贝>7.Copy Merged<合并拷贝>8.Paste<粘贴>9.Paste Into<粘贴入>10.Clear<清除>11.Fill<填充>12.Stroke<描边>13.Free Transform<自由变形>14.Transform<变换><1>Again<再次><2>Sacle<缩放><3>Rotate<旋转><4>Skew<斜切><5>Distort<扭曲><6>Prespective<透视><7>Rotate 180°<旋转180度><8>Rotate 90°CW<顺时针旋转90度><9>Rotate 90°CCW<逆时针旋转90度><10> Flip Hpeizontal<水平翻转><11> Flip Vertical<垂直翻转>15.Define Brush<定义画笔>16.Define Pattern<设置图案>17.Define Custom Shape<定义自定形状>18.Purge<清除内存数据><1> Undo<还原><2> Clipboard<剪贴板><3> Histories<历史纪录><4> All<全部>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<8> Memory ＆ Image Cache<内存和图像高速缓存><9> Adobe Online<10> Workflows Options<工作流程选项>三、Image<图像>1.Mode<模式><1> Bitmap<位图><2> Grayscale<灰度><3> Duotone<双色调><4> Indexed Color<索引色><5> RGB Color<6> CMYK Color<7> Lab Color<8> Multichannel<多通道><9> 8 Bits/Channel<8位通道><10> 16 Bits/Channel<16位通道><11> Color Table<颜色表><12>Assing Profile<制定配置文件><13>Convert to Profile<转换为配置文件> 2.Adjust<调整><1> Levels<色阶>><2> Auto Laves<自动色阶><3> Auto Contrast<自动对比度><4> Curves<曲线>><5> Color Balance<色彩平衡><6> Brightness/Contrast<亮度/对比度><7> Hue/Saturation<色相/饱和度><8> Desaturate<去色><9> Replace Color<替换颜色><10> Selective Color<可选颜色><11> Channel Mixer<通道混合器><12> Gradient Map<渐变映射><13> Invert<反相><14> Equalize<色彩均化><15> Threshold<阈值><16> Posterize<色调分离><17> Variations<变化>3.Duplicate<复制>4.Apply Image<应用图像>5.Calculations<计算>6.Image Size<图像大小>7.Canvas Size<画布大小>8.Rotate Canvas<旋转画布><1> 180°<180度><2> 90°CW<顺时针90度><3> 90°CCW<逆时针90度><4> Arbitrary<任意角度><5> Flip Horizontal<水平翻转><6> Flip Vertical<垂直翻转>9.Crop<裁切>10.Trim<修整>11.Reverl All<显示全部>12.Histogram<直方图>13.Trap<陷印>14.Extract<抽出>15.Liquify<液化>四、Layer<图层>1.New<新建><1> Layer<图层><2> Background From Layer<背景图层><3> Layer Set<图层组><4> Layer Set From Linked<图层组来自链接的><5> Layer via Copy<通过拷贝的图层><6> Layer via Cut<通过剪切的图层>2.Duplicate Layer<复制图层>3.Delete Layer<删除图层>yer Properties<图层属性>yer style<图层样式><1> Blending Options<混合选项><2> Drop Shadow<投影><3> Inner Shadow<内阴影><4> Outer Glow<外发光><5> Inner Glow<内发光><6> Bevel and Emboss<斜面和浮雕><7> Satin<光泽><8> Color Overlay<颜色叠加><9> Gradient Overlay<渐变叠加><10> Pattern Overlay<图案叠加><11> Stroke<描边><12> Copy Layer Effects<拷贝图层样式><13> Paste Layer Effects<粘贴图层样式><14> Paste Layer Effects To Linked<将图层样式粘贴的链接的><15> Clear Layer Effects<清除图层样式><16> Global Light<全局光><17> Create Layer<创建图层><18> Hide All Effects<显示/隐藏全部效果><19> Scale Effects<缩放效果>6.New Fill Layer<新填充图层><1> Solid Color<纯色><2> Gradient<渐变><3> Pattern<图案>7.New Adjustment Layer<新调整图层><1>Levels<色阶><2>Curves<曲线><3>Color Balance<色彩平衡><4>Brightness/Contrast<亮度/对比度><5>Hue/Saturation<色相/饱和度><6>Selective Color<可选颜色><7>Channel Mixer<通道混合器><8>Gradient Map<渐变映射><9>Invert<反相><10>Threshold<阈值><11>Posterize<色调分离>8.Change Layer Content<更改图层内容>yer Content Options<图层内容选项>10.Type<文字><1> Create Work Path<创建工作路径><2> Convert to Shape<转变为形状><3> Horizontal<水平><4> Vertical<垂直><5> Anti-Alias None<消除锯齿无><6> Anti-Alias Crisp<消除锯齿明晰><7> Anti-Alias Strong<消除锯齿强><8> Anti-Alias Smooth<消除锯齿平滑><9> Covert T o Paragraph Text<转换为段落文字><10> Warp Text<文字变形><11>Update All Text Layers<更新所有文本图层><12>Replace All Missing Fonts<替换所以缺欠文字> 11.Rasterize<栅格化><1>Type<文字><2>Shape<形状><3>Fill Content<填充内容><4>Layer Clipping Path<图层剪贴路径><5>Layer<图层><6>Linked Layers<链接图层><7>All Layers<所以图层>12.New Layer Based Slice<基于图层的切片>13.Add Layer Mask<添加图层蒙板><1> Reveal All<显示全部><2> Hide All<隐藏全部><3> Reveal Selection<显示选区><4> Hide Selection<隐藏选区>14.Enable Layer Mask<启用图层蒙板>15.Add Layer Clipping Path<添加图层剪切路径><1>Reveal All<显示全部><2>Hide All<隐藏全部><3>Current Path<当前路径>16.Enable Layer Clipping Path<启用图层剪切路径>17.Group Linked<于前一图层编组>18.UnGroup<取消编组>19.Arrange<排列><1> Bring to Front<置为顶层><2> Bring Forward<前移一层><3> Send Backward<后移一层><4> Send to Back<置为底层>20.Arrange Linked<对齐链接图层><1> Top Edges<顶边><2> Vertical Center<垂直居中><3> Bottom Edges<底边><4> Left Edges<左边><5> Horizontal Center<水平居中><6> Right Edges<右边>21.Distribute Linked<分布链接的><1> Top Edges<顶边><2> Vertical Center<垂直居中><3> Bottom Edges<底边><4> Left Edges<左边><5> Horizontal Center<水平居中><6> Right Edges<右边>22.Lock All Linked Layers<锁定所有链接图层>23.Merge Linked<合并链接图层>24.Merge Visible<合并可见图层>25.Flatten Image<合并图层>26.Matting<修边><1> Define<去边><2> Remove Black Matte<移去黑色杂边><3> Remove White Matte<移去白色杂边>五、Selection<选择>1.All<全部>2.Deselect<取消选择>3.Reselect<重新选择>4.Inverse<反选>5.Color Range<色彩范围>6.Feather<羽化>7.Modify<修改><1> Border<扩边><2> Smooth<平滑><3> Expand<扩展><4> Contract<收缩>8.Grow<扩大选区>9.Similar<选区相似>10.Transform Selection<变换选区>11.Load Selection<载入选区>12.Save Selection<存储选区>六、Filter<滤镜>st Filter<上次滤镜操作>2.Artistic<艺术效果><1> Colored Pencil<彩色铅笔><2> Cutout<剪贴画><3> Dry Brush<干笔画><4> Film Grain<胶片颗粒><5> Fresco<壁画><6> Neon Glow<霓虹灯光><7> Paint Daubs<涂抹棒><8> Palette Knife<调色刀><9> Plastic Wrap<塑料包装><10> Poster Edges<海报边缘><11> Rough Pastels<粗糙彩笔><12> Smudge Stick<绘画涂抹><13> Sponge<海绵><14> Underpainting<底纹效果><15> Watercolor<水彩>3.Blur<模糊><1> Blur<模糊><2> Blur More<进一步模糊><3> Gaussian Blur<高斯模糊><4> Motion Blur<动态模糊><5> Radial Blur<径向模糊><6> Smart Blur<特殊模糊>4.Brush Strokes<画笔描边><1> Accented Edges<强化边缘><2> Angled Stroke<成角的线条><3> Crosshatch<阴影线><4> Dark Strokes<深色线条><5> Ink Outlines<油墨概况><6> Spatter<喷笔><7> Sprayed Strokes<喷色线条><8> Sumi5.Distort<扭曲><1> Diffuse Glow<扩散亮光><2> Displace<置换><3> Glass<玻璃><4> Ocean Ripple<海洋波纹><5> Pinch<挤压><6> Polar Coordinates<极坐标><7> Ripple<波纹><8> Shear<切变><9> Spherize<球面化><10> Twirl<旋转扭曲><11> Wave<波浪><12> Zigzag<水波>6.Noise<杂色><1> Add Noise<加入杂色><2> Despeckle<去斑><3> Dust ＆ Scratches<蒙尘与划痕><4> Median<中间值>7.Pixelate<像素化><1> Color Halftone<彩色半调><2> Crystallize<晶格化><3> Facet<彩块化><4> Fragment<碎片><5> Mezzotint<铜版雕刻><6> Mosaic<马赛克><7> Pointillize<点状化>8.Render<渲染><1> 3D Transform<3D 变换><2> Clouds<云彩><3> Difference Clouds<分层云彩><4> Lens Flare<镜头光晕><5> Lighting Effects<光照效果><6> Texture Fill<纹理填充>9.Sharpen<锐化><1> Sharpen<锐化><2> Sharpen Edges<锐化边缘><3> Sharpen More<进一步锐化><4> Unsharp Mask10.Sketch<素描><1> Bas Relief<基底凸现><2> Chalk ＆ Charcoal<粉笔和炭笔><3> Charcoal<3> Chrome<铬黄><4> Conte Crayon<彩色粉笔><5> Graphic Pen<绘图笔><6> Halftone Pattern<半色调图案><7> Note Paper<便条纸><8> Photocopy<副本><9> Plaster<塑料效果><10> Reticulation<网状><11> Stamp<图章><12> Torn Edges<撕边><13> Water Paper<水彩纸>11.Stylize<风格化><1> Diffuse<扩散><2> Emboss<浮雕><3> Extrude<突出><4> Find Edges<查找边缘><5> Glowing Edges<照亮边缘><6> Solarize<曝光过度><7> Tiles<拼贴><8> Trace Contour<等高线><9> Wind<风>12.Texture<<纹理><1> Craquelure<龟裂缝><2> Grain<颗粒><3> Mosained Tiles<马赛克拼贴><4> Patchwork<拼缀图><5> Stained Glass<染色玻璃><6> Texturixer<纹理化>13.Video<视频><1> De<2> NTSC Colors14.Other<其它><1> 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.Digimarc<1>Embed Watermark<嵌入水印><2>Read Watermark<读取水印>七、View<视图>1.New View<新视图>2.Proof Setup<校样设置><1>Custom<自定><2>Working CMYK<处理CMYK><3>Working Cyan Plate<处理青版><4>Working Magenta Plate<处理洋红版><5>Working Yellow Plate<处理黄版><6>Working Black Plate<处理黑版><7>Working CMY Plate<处理CMY版><8>Macintosh RGB<9>Windows RGB<10>Monitor RGB<显示器RGB><11>Simulate Paper White<模拟纸白><12>Simulate Ink Black<模拟墨黑>3.Proof Color<校样颜色>4.Gamut Wiring<色域警告>5.Zoom In<放大>6.Zoom Out<缩小>7.Fit on Screen<满画布显示>8.Actual Pixels<实际象素>9.Print Size<打印尺寸>10.Show Extras<显示额外的>11.Show<显示><1> Selection Edges<选区边缘><2> Target Path<目标路径><3> Grid<网格><4> Guides<参考线><5> Slices<切片><6> Notes<注释><7> All<全部><8> None<无><9>Show Extras Options<显示额外选项>12.Show Rulers<显示标尺>13.Snap<对齐>14.Snap To<对齐到><1> Guides<参考线><2> Grid<网格>。
图像处理专业英语词汇
FFT 滤波器FFT filtersVGA 调色板和许多其他参数VGA palette and many others 按名称排序sort by name包括角度和刻度including angle and scale保持目标keep targets保存save保存和装载save and load饱和度saturation饱和加法和减法add and subtract with saturate背景淡化background flatten背景发现find background边缘和条纹测量Edge and Stripe/Measurement边缘和条纹的提取find edge and stripe编辑Edit编辑edit编辑或删除相关区域edit or delete relative region编码Code编码条Coda Bar变换forward or reverse fast Fourier transformation变量和自定义的行为variables and custom actions变量检测examine variables变形warping变形系数warping coefficients标题tile标注和影响区域label and zone of influence标准normal标准偏差standard deviation表面弯曲convex并入图像merge to image采集栏digitizer bar采集类型grab type菜单形式menu item参数Preferences参数轴和角度reference axis and angle测量measurement测量方法提取extract measurements from测量结果显示和统计display measurement results and statistics测量转换transfer to measurement插入Insert插入条件检查Insert condition checks查找最大值find extreme maximum长度length超过50 个不同特征的计算calculate over 50 different features area 撤销次数number of undo levels乘multiply尺寸size处理Processing处理/采集图像到一个新的窗口processed/grabbed image into new window 窗口window窗口监视watch window窗位window leveling创建create垂直边沿vertical edge从表格新建new from grid从工具条按钮from toolbar button从用户窗口融合merge from user form粗糙roughness错误纠正error correction错误匹配fit error打开open打开近期的文件或脚本open recent file or script打印print打印设置print setup打印预览print preview大小和日期size and date带通band pass带有调色板的8- bit带有动态预览的直方图和x, y 线曲线椭圆轮廓histogram and x, y line curve ellipse profiles with dynamic preview带阻band reject代码类型code type单步single step单一simple单帧采集snap shot导入VB等等etc.低通low pass第一帧first点point调色板预览palette viewer调试方式debug mode调用外部的DLL调整大小resize调整轮廓滤波器的平滑度和轮廓的最小域值adjust smoothness of contour filter and minimum threshold for contours定点除fixed point divide定位精度positional accuracy定义一个包含有不相关的不一致的或无特征区域的模板define model including mask for irrelevant inconsistent or featureless areas定制制定-配置菜单Customize - configure menus动态预览with dynamic preview读出或产生一个条形或矩阵码read or generate bar and matrix codes读取和查验特征字符串erify character strings断点break points对比度contrast对比度拉伸contrast stretch对称symmetry对模板应用“不关心的”像素标注apply don't care pixel mask to model 多边形polygon二进制binary二进制分离separate binary二值和灰度binary and grayscale翻转reverse返回return放大或缩小7 个级别zoom in or out 7 levels分类结果sort results分水岭Watershed分析Analysis分组视图view components浮点float腐蚀erode复合视图view composite复合输入combined with input复制duplicate复制duplicateselect all傅立叶变换Fourier transform改变热点值change hotspot values感兴趣区域ROI高级几何学Advanced geometry高通high pass格式栏formatbar更改默认的搜索参数modify default search parameters 工具Utilities工具栏toolbar工具属性tool properties工具条toolbar工作区workspace bar共享轮廓shared contours构件build构造表格construct grid关闭close和/或and/or和逆FFT画图工具drawing tools缓存buffer换算convert灰度grayscale恢复目标restore targets回放playback绘图连结connect map获得/装载标注make/load mask获取选定粒子draw selected blobs或从一个相关区域创建一个ROI or create an ROI from a relative region基线score基于校准映射的畸变校正distortion correction based on calibration mapping 极性polarity极坐标转换polar coordinate transformation几何学Geometry记录record加粗thick加法add间隔spacing间距distance兼容compatible简洁compactness剪切cut减法subtract减小缩进outdent交互式的定义字体参数包括搜索限制ine font parameters including search constraints 脚本栏script bar角度angle角度和缩放范围angle and scale range接收和确定域值acceptance and certainty thresholds结果栏result bar解开目标unlock targets精确度和时间间隔accuracy and timeout interval矩形rectangle矩形rectangular绝对差分absolute difference绝对值absolute value均匀uniform均值average拷贝copy拷贝序列copy sequence可接收的域值acceptance threshold克隆clone控制control控制controls快捷健shortcut key宽度breadth宽度width拉普拉斯Laplacians拉伸elongation蓝blue类型type粒子blob粒子标注label blobs粒子分离segment blobs粒子内的孔数目number of holes in a blob 亮度brightness亮度luminance另存为save as滤波器filters绿green轮廓profile overlay轮廓极性contour polarity逻辑运算logical operations面积area模板编辑edit model模板覆盖model coverage模板和目标覆盖model and target coverage 模板索引model index模板探测器Model Finder模板位置和角度model position and angle 模板中心model center模糊mask模块import VB module模块modules模式匹配Pattern matching默认案例default cases目标Targets目标分离separate objects目标评价target score欧拉数Euler number盆basins膨胀dilate匹配率match scores匹配数目number of matches平方和sum of the squares平滑smooth平均average平均averaged平均值mean平移translation前景色foreground color清除缓冲区为一个恒量clear buffer to a constant清除特定部分delete special区域增长region-growing ROI取反negate全部删除delete all缺省填充和相连粒子分离fill holes and separate touching blobs任意指定位置的中心矩和二阶矩central and ordinary moments of any order location: X, Y 锐化sharpen三维视图view 3D色度hue删除delete删除帧delete frame设置settings设置相机类型enable digitizer camera type设置要点set main示例demos事件发现数量number of occurrences事件数目number of occurrences视图View收藏collectionDICOM手动manually手绘曲线freehand输出选项output options输出选择结果export selected results输入通道input channel属性页properties page数据矩阵DataMatrix数字化设置Digitizer settings双缓存double buffer双域值two-level水平边沿horizontal edge搜索find搜索和其他应用Windows Finder and other applications 搜索角度search angle搜索结果search results搜索区域search area搜索区域search region搜索速度search speed速度speed算法arithmetic缩放scaling缩放和偏移scale and offset锁定目标lock destination锁定实时图像处理效果预览lock live preview of processing effects on images 锁定预览Lock preview锁定源lock source特定角度at specific angle特定匹配操作hit or miss梯度rank替换replace添加噪声add noise条带直径ferret diameter停止stop停止采集halt grab同步synchronize同步通道sync channel统计Statistics图像Image图像大小image size图像拷贝copy image图像属性image properties图形graph退出exit椭圆ellipse椭圆ellipses外形shape伪彩pseudo-color位置position文本查看view as text文件File文件MIL MFO font file文件load and save as MIL MMF files文件load and save models as MIL MMO files OCR文件中的函数make calls to functions in external DLL files文件转换器file converterActiveMIL Builder ActiveMIL Builder 无符号抽取部分Extract band -细化thin下一帧next显示表现字体的灰度级ayscale representations of fonts显示代码show code线line线lines相对起点relative origin像素总数sum of all pixels向前或向后移动Move to front or back向上或向下up or down校准Calibration校准calibrate新的/感兴趣区域粘贴paste into New/ROI新建new信息/ 图形层DICOM information/overlay形态morphology行为actions修改modify修改路径modify paths修改搜索参数modify default search parameters 序列采集sequence旋转rotation旋转模板rotate model选择select选择selector循环loops移动move移动shift应用过滤器和分类器apply filters and classifiers 影响区域zone of influence映射mapping用户定义user defined用基于变化上的控制实时预览分水岭转化结果阻止过分切割live preview of resulting watershed transformations with control over variation to prevent over segmentation用某个值填充fill with value优化和编辑调色板palette optimization/editor有条件的conditional域值threshold预处理模板优化搜索速度循环全部扫描preprocess model to optimize search speed circular over-scan预览previous元件数目和开始（自动或手动）number of cells and threshold auto or manual元件最小／最大尺寸cell size min/max源source允许的匹配错误率和加权fit error and weight运行run在目标中匹配数目number of modelmatches in target暂停pause增大缩进indent整数除integer divide正FFT正常连续continuous normal支持象征学supported symbologies: BC 412直方图均衡histogram equalization执行execute执行外部程序和自动完成VBA only execute external programs and perform Automation VBA only指定specify指数exponential Rayleigh中值median重复repeat重建reconstruct重建和修改字体restore and modify fonts重新操作redo重心center of gravity周长perimeter注释annotations转换Convert转换convert装载load装载和保存模板为MIL MMO装载和另存为MIL MFO装载和另存为MIL MMF状态栏status bar资源管理器拖放图像drag-and-drop images from Windows ExplorerWindows自动或手动automatic or manual自动或手动模板创建automatic or manual model creation字符产大小string size字符串string字体font最大maximum最大化maximum最大数maxima最后一帧last frame最小minimum最小化minimum最小间隔标准minimum separation criteria最小数minima坐标盒的范围bounding box coordinates图像数据操作Image data manipulation内存分配与释放allocation release图像复制copying设定和转换setting and conversion图像/视频的输入输出Image and video I/O支持文件或摄像头的输入file and camera based input图像/视频文件的输出image/video file output矩阵/向量数据操作及线性代数运算Matrix and vector manipulation and linear algebra routines 矩阵乘积products矩阵方程求解solvers特征值eigenvalues奇异值分解SVD支持多种动态数据结构Various dynamic data structures 链表lists队列queues数据集sets树trees图graphs基本图像处理Basic image processing去噪filtering边缘检测edge detection角点检测corner detection采样与插值sampling and interpolation色彩变换color conversion形态学处理morphological operations直方图histograms图像金字塔结构image pyramids结构分析Structural analysis连通域/分支connected components轮廓处理contour processing距离转换distance transform图像矩various moments模板匹配template matching霍夫变换Hough transform多项式逼近polygonal approximation曲线拟合line fitting椭圆拟合ellipse fitting狄劳尼三角化Delaunay triangulation摄像头定标Camera calibration寻找和跟踪定标模式finding and tracking calibration patterns 参数定标calibration,基本矩阵估计fundamental matrix estimation单应矩阵估计homography estimation立体视觉匹配stereo correspondence）运动分析Motion analysis光流optical flow动作分割motion segmentation目标跟踪tracking目标识别Object recognition特征方法eigen-methodsHMM模型HMM基本的GUI Basic GUI显示图像/视频display image/video键盘/鼠标操作keyboard and mouse handling滑动条scroll-bars图像标注Image labeling直线line曲线conic多边形polygon、文本标注text drawing梯度方向gradient directions系数coefficient空间频率spatial frequencies串级过滤cascade filtering卷积运算convolution operation有限差分近似the finite difference approximation 对数刻度logarithmic scale仿射参数affine parameters斑点Blob差距disparityAlgebraic operation 代数运算；一种图像处理运算，包括两幅图像对应像素的和、差、积、商。
图像处理
图像处理(image processing)定义用计算机对图像进行分析，以达到所需结果的技术。
又称影像处理。
基本内容图像处理一般指数字图像处理。
数字图像是指用数字摄像机、扫描仪等设备经过采样和数字化得到的一个大的二维数组，该数组的元素称为像素，其值为一整数，称为灰度值。
图像处理技术的主要内容包括图像压缩，增强和复原，匹配、描述和识别3个部分。
图像压缩由数字化得到的一幅图像的数据量十分巨大，一幅典型的数字图像通常由500×500或1000×1000个像素组成。
如果是动态图像，是其数据量更大。
因此图像压缩对于图像的存储和传输都十分必要。
有两类压缩算法，即不失真的方法和近似的方法。
最常用的不失真压缩取空间或时间上相邻像素值的差，再进行编码。
游程码就是这类压缩码的例子。
近似压缩算法大都采用图像交换的途径，例如对图像进行快速傅里叶变换或离散的余弦变换。
著名的、已作为图像压缩国际标准的JPEG和MPEG均属于近似压缩算法。
前者用于静态图像，后者用于动态图像。
它们已由芯片实现。
图像增强和复原图像增强的目标是改进图片的质量，例如增加对比度，去掉模糊和噪声，修正几何畸变等；图像复原是在假定已知模糊或噪声的模型时，试图估计原图像的一种技术。
图像增强按所用方法可分成频率域法和空间域法。
前者把图像看成一种二维信号，对其进行基于二维傅里叶变换的信号增强。
采用低通滤波（即只让低频信号通过）法，可去掉图中的噪声；采用高通滤波法，则可增强边缘等高频信号，使模糊的图片变得清晰。
具有代表性的空间域算法有局部求平均值法和中值滤波（取局部邻域中的中间像素值）法等，它们可用于去除或减弱噪声。
早期的数字图像复原亦来自频率域的概念。
现代采取的是一种代数的方法，即通过解一个大的方程组来复原理想的图片。
图像匹配、描述和识别对图像进行比较和配准，通过分制提取图像的特征及相互关系，得到图像符号化的描述，再把它同模型比较，以确定其分类。
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。
patch 图像处理 翻译
patch 图像处理翻译基本解释●patch：补丁，修补●/pætʃ/●n. 补丁，修补程序●v. 修补，打补丁变化形式●n. 复数形式：patches●v. 第三人称单数：patches●过去式：patched●过去分词：patched●现在分词：patching具体用法●●n.:o补丁，修补程序o同义词：piece, segment, section, fragment, portion o反义词：whole, entirety, totality, completeness, unityo例句：●The software update included a patch to fix the securityvulnerabilities that were discovered last month. (软件更新包含一个补丁，用于修复上个月发现的安全漏洞。
)●He sewed a patch onto his jeans to cover the hole that hadformed over time. (他在牛仔裤上缝了一个补丁，以覆盖随着时间形成的洞。
)●The garden had a patch of sunflowers that stood out brightlyagainst the green grass. (花园里有一片向日葵，在绿色的草地上显得格外明亮。
)●The doctor applied a patch to the patient's skin to delivermedication over a period of time. (医生在病人的皮肤上贴了一个补丁，以在一段时间内输送药物。
)●The quilt was made up of various patches of fabric, each withits own unique pattern. (被子由各种布料补丁组成，每块都有自己独特的图案。
BANet图像处理
BANet图像处理
图像处理(imageprocessing)，用计算机对图像进行分析，以达到所需结果的技术。
又称影像处理。
图像处理一般指数字图像处理。
数字图像是指用工业相机、摄像机、扫描仪等设备经过拍摄得到的一个大的二维数组，该数组的元素称为像素，其值称为灰度值。
图像处理技术一般包括图像压缩，增强和复原，匹配、描述和识别3个部分。
首先数字图像处理技术可以帮助人们更客观、准确地认识世界，人的视觉系统可以帮助人类从外界获取3/4以上的信息，而图像、图形又是所有视觉信息的载体，尽管人眼的鉴别力很高，可以识别上千种颜色，但很多情况下，图像对于人眼来说是模糊的甚至是不可见的，通过图象增强技术，可以使模糊甚至不可见的图像变得清晰明亮。
在计算机中，按照颜色和灰度的多少可以将图像分为二值图像、灰度图像、索引图像和真彩色RGB图像四种基本类型。
大多数图像处理软件都支持这四种类型的图像。
2018年关于图像的英文翻译-精选word文档 (2页)
2018年关于图像的英文翻译-精选word文档
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关于图像的英文翻译
图像的英文：
image
picture
graphic
参考例句：
Image scanner
图像扫描仪Picture processing
图像处理Animated graphics
活动图像Contiguous graphics
连通图像vision transmitter
图像发射机 vision amplifier
图像放大器 veiled sounds; the image is veiled or foggy.
模糊的声音；图像模糊、朦胧。
The computer programmer morphed the image.
计算机程序设计器使图像变形了。
No one was able to make head or tail of the figures.
这些图像谁也看不明白。
It is the microcosmic image of the macrocosm of the entire planet.
它是整个行星宏观世界的微观图像。
image是什么意思：。
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数字图像处理英文翻译（Matlab帮助信息简介）xxxxxxxxx xxx IntroductionMATLAB is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis, and numeric computation. Using the MATLAB product, you can solve technical computing problems faster than with traditional programming languages, such as C, C++, and Fortran.You can use MATLAB in a wide range of applications, including signal and image processing, communications, control design, test and measurement, financial modeling and analysis, and computational biology. Add-on toolboxes (collections of special-purpose MATLAB functions, available separately) extend the MATLAB environment to solve particular classes of problems in these application areas.The MATLAB system consists of these main parts:Desktop Tools and Development EnvironmentThis part of MATLAB is the set of tools and facilities that help you use and become more productive with MATLAB functions and files. Many of these tools are graphical user interfaces. It includes: theMATLAB desktop and Command Window, an editor and debugger, a code analyzer, and browsers for viewing help, the workspace, and folders. Mathematical Function LibraryThis library is a vast collection of computational algorithms ranging from elementary functions, like sum, sine, cosine, and complex arithmetic, to more sophisticated functions like matrix inverse, matrix eigenvalues, Bessel functions, and fast Fourier transforms.The LanguageThe MATLAB language is a high-level matrix/array language with control flow statements, functions, data structures, input/output, and object-oriented programming features. It allows both "programming in the small" to rapidly create quick programs you do not intend to reuse. You can also do "programming in the large" to create complex application programs intended for reuse.GraphicsMATLAB has extensive facilities for displaying vectors and matrices as graphs, as well as annotating and printing these graphs. It includes high-level functions for two-dimensional and three-dimensional data visualization, image processing, animation, and presentation graphics. Italso includes low-level functions that allow you to fully customize the appearance of graphics as well as to build complete graphical user interfaces on your MATLAB applications.External InterfacesThe external interfaces library allows you to write C/C++ and Fortran programs that interact with MATLAB. It includes facilities for calling routines from MATLAB (dynamic linking), for calling MATLAB as a computational engine, and for reading and writing MAT-files.MATLAB provides a number of features for documenting and sharing your work. You can integrate your MATLAB code with other languages and applications, and distribute your MATLAB algorithms and applications. Features include:High-level language for technical computingDevelopment environment for managing code, files, and dataInteractive tools for iterative exploration, design, and problem solving Mathematical functions for linear algebra, statistics, Fourier analysis, filtering, optimization, and numerical integration2-D and 3-D graphics functions for visualizing dataTools for building custom graphical user interfacesFunctions for integrating MATLAB based algorithms with external appli cations and languages, such as C, C++, Fortran, Java™, COM, andMicrosoft® ExcelThe basic data structure in MATLAB is the array, an ordered set of real or complex elements. This object is naturally suited to the representation of images, real-valued ordered sets of color or intensity data.MATLAB stores most images as two-dimensional arrays (i.e., matrices), in which each element of the matrix corresponds to a single pixel in the displayed image. (Pixel is derived from picture element and usually denotes a single dot on a computer display.)For example, an image composed of 200 rows and 300 columns of different colored dots would be stored in MATLAB as a 200-by-300 matrix. Some images, such as truecolor images, require a three-dimensional array, where the first plane in the third dimension represents the red pixel intensities, the second plane represents the green pixel intensities, and the third plane represents the blue pixel intensities. This convention makes working with images in MATLAB similar to working with any other type of matrix data, and makes the full power of MATLAB available for image processing applications.The Image Processing Toolbox software is a collection of functions that extend the capability of the MATLAB numeric computing environment. The toolbox supports a wide range of image processing operations, includingSpatial image transformationsMorphological operationsNeighborhood and block operationsLinear filtering and filter designTransformsImage analysis and enhancementImage registrationDeblurringRegion of interest operationsMany of the toolbox functions are MATLAB files with a series of MATLAB statements that implement specialized image processing algorithms. You can view the MATLAB code for these functions using the statement:type function_nameYou can extend the capabilities of the toolbox by writing your own files, or by using the toolbox in combination with other toolboxes, such as the Signal Processing Toolbox™ software and the Wavelet Toolbox™ software.Configuration NotesTo determine if the Image Processing Toolbox software is installed on your system, type this command at the MATLAB prompt.verWhen you enter this command, MATLAB displays information about the version of MATLAB you are running, including a list of all toolboxes installed on your system and their version numbers.For information about installing the toolbox, see the installation guide.For the most up-to-date information about system requirements, see the system requirements page, available in the products area at the MathWorks Web site ().Related ProductsMathWorks provides several products that are relevant to the kinds of tasks you can perform with the Image Processing Toolbox software and that extend the capabilities of MATLAB. For information about these related products, see /products/image/related.html. CompilabilityThe Image Processing Toolbox software is compilable with the MATLAB Compiler except for the following functions that launch GUIs cpselectimplayimtool。