数字图像处理外文翻译参考文献

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人脸识别面部数字图像处理相关中英对照外文文献翻译毕业设计论文高质量人工翻译原文带出处

人脸识别相关文献翻译，纯手工翻译，带原文出处(原文及译文)如下翻译原文来自Thomas David Heseltine BSc. Hons. The University of YorkDepartment of Computer ScienceFor the Qualification of PhD. — September 2005 -《Face Recognition: Two-Dimensional and Three-Dimensional Techniques》4 Two-dimensional Face Recognition4.1 Feature LocalizationBefore discussing the methods of comparing two facial images we now take a brief look at some at the preliminary processes of facial feature alignment. This process typically consists of two stages: face detection and eye localisation. Depending on the application, if the position of the face within the image is known beforehand (fbr a cooperative subject in a door access system fbr example) then the face detection stage can often be skipped, as the region of interest is already known. Therefore, we discuss eye localisation here, with a brief discussion of face detection in the literature review(section 3.1.1).The eye localisation method is used to align the 2D face images of the various test sets used throughout this section. However, to ensure that all results presented are representative of the face recognition accuracy and not a product of the performance of the eye localisation routine, all image alignments are manually checked and any errors corrected, prior to testing and evaluation.We detect the position of the eyes within an image using a simple template based method. A training set of manually pre-aligned images of feces is taken, and each image cropped to an area around both eyes. The average image is calculated and used as a template.Figure 4-1 - The average eyes. Used as a template for eye detection.Both eyes are included in a single template, rather than individually searching for each eye in turn, as the characteristic symmetry of the eyes either side of the nose, provides a useful feature that helps distinguish between the eyes and other false positives that may be picked up in the background. Although this method is highly susceptible to scale(i.e. subject distance from the camera) and also introduces the assumption that eyes in the image appear near horizontal. Some preliminary experimentation also reveals that it is advantageous to include the area of skin justbeneath the eyes. The reason being that in some cases the eyebrows can closely match the template, particularly if there are shadows in the eye-sockets, but the area of skin below the eyes helps to distinguish the eyes from eyebrows (the area just below the eyebrows contain eyes, whereas the area below the eyes contains only plain skin).A window is passed over the test images and the absolute difference taken to that of the average eye image shown above. The area of the image with the lowest difference is taken as the region of interest containing the eyes. Applying the same procedure using a smaller template of the individual left and right eyes then refines each eye position.This basic template-based method of eye localisation, although providing fairly preciselocalisations, often fails to locate the eyes completely. However, we are able to improve performance by including a weighting scheme.Eye localisation is performed on the set of training images, which is then separated into two sets: those in which eye detection was successful; and those in which eye detection failed. Taking the set of successful localisations we compute the average distance from the eye template (Figure 4-2 top). Note that the image is quite dark, indicating that the detected eyes correlate closely to the eye template, as we would expect. However, bright points do occur near the whites of the eye, suggesting that this area is often inconsistent, varying greatly from the average eye template.Figure 4-2 一Distance to the eye template for successful detections (top) indicating variance due to noise and failed detections (bottom) showing credible variance due to miss-detected features.In the lower image (Figure 4-2 bottom), we have taken the set of failed localisations(images of the forehead, nose, cheeks, background etc. falsely detected by the localisation routine) and once again computed the average distance from the eye template. The bright pupils surrounded by darker areas indicate that a failed match is often due to the high correlation of the nose and cheekbone regions overwhelming the poorly correlated pupils. Wanting to emphasise the difference of the pupil regions for these failed matches and minimise the variance of the whites of the eyes for successful matches, we divide the lower image values by the upper image to produce a weights vector as shown in Figure 4-3. When applied to the difference image before summing a total error, this weighting scheme provides a much improved detection rate.Figure 4-3 - Eye template weights used to give higher priority to those pixels that best represent the eyes.4.2 The Direct Correlation ApproachWe begin our investigation into face recognition with perhaps the simplest approach,known as the direct correlation method (also referred to as template matching by Brunelli and Poggio [29 ]) involving the direct comparison of pixel intensity values taken from facial images. We use the term "Direct Conelation, to encompass all techniques in which face images are compared directly, without any form of image space analysis, weighting schemes or feature extraction, regardless of the distance metric used. Therefore, we do not infer that Pearson's correlation is applied as the similarity function (although such an approach would obviously come under our definition of direct correlation). We typically use the Euclidean distance as our metric in these investigations (inversely related to Pearson's correlation and can be considered as a scale and translation sensitive form of image correlation), as this persists with the contrast made between image space and subspace approaches in later sections.Firstly, all facial images must be aligned such that the eye centres are located at two specified pixel coordinates and the image cropped to remove any background information. These images are stored as greyscale bitmaps of 65 by 82 pixels and prior to recognition converted into a vector of 5330 elements (each element containing the corresponding pixel intensity value). Each corresponding vector can be thought of as describing a point within a 5330 dimensional image space. This simple principle can easily be extended to much larger images: a 256 by 256 pixel image occupies a single point in 65,536-dimensional image space and again, similar images occupy close points within that space. Likewise, similar faces are located close together within the image space, while dissimilar faces are spaced far apart. Calculating the Euclidean distance d, between two facial image vectors (often referred to as the query image q, and gallery image g), we get an indication of similarity. A threshold is then applied to make the final verification decision.d . q - g ( threshold accept ) (d threshold ⇒ reject ). Equ. 4-14.2.1 Verification TestsThe primary concern in any face recognition system is its ability to correctly verify a claimed identity or determine a person's most likely identity from a set of potential matches in a database. In order to assess a given system's ability to perform these tasks, a variety of evaluation methodologies have arisen. Some of these analysis methods simulate a specific mode of operation (i.e. secure site access or surveillance), while others provide a more mathematicaldescription of data distribution in some classification space. In addition, the results generated from each analysis method may be presented in a variety of formats. Throughout the experimentations in this thesis, we primarily use the verification test as our method of analysis and comparison, although we also use Fisher's Linear Discriminant to analyse individual subspace components in section 7 and the identification test for the final evaluations described in section 8. The verification test measures a system's ability to correctly accept or reject the proposed identity of an individual. At a functional level, this reduces to two images being presented for comparison, fbr which the system must return either an acceptance (the two images are of the same person) or rejection (the two images are of different people). The test is designed to simulate the application area of secure site access. In this scenario, a subject will present some form of identification at a point of entry, perhaps as a swipe card, proximity chip or PIN number. This number is then used to retrieve a stored image from a database of known subjects (often referred to as the target or gallery image) and compared with a live image captured at the point of entry (the query image). Access is then granted depending on the acceptance/rej ection decision.The results of the test are calculated according to how many times the accept/reject decision is made correctly. In order to execute this test we must first define our test set of face images. Although the number of images in the test set does not affect the results produced (as the error rates are specified as percentages of image comparisons), it is important to ensure that the test set is sufficiently large such that statistical anomalies become insignificant (fbr example, a couple of badly aligned images matching well). Also, the type of images (high variation in lighting, partial occlusions etc.) will significantly alter the results of the test. Therefore, in order to compare multiple face recognition systems, they must be applied to the same test set.However, it should also be noted that if the results are to be representative of system performance in a real world situation, then the test data should be captured under precisely the same circumstances as in the application environment.On the other hand, if the purpose of the experimentation is to evaluate and improve a method of face recognition, which may be applied to a range of application environments, then the test data should present the range of difficulties that are to be overcome. This may mean including a greater percentage of6difficult9 images than would be expected in the perceived operating conditions and hence higher error rates in the results produced. Below we provide the algorithm for executing the verification test. The algorithm is applied to a single test set of face images, using a single function call to the face recognition algorithm: CompareF aces(F ace A, FaceB). This call is used to compare two facial images, returning a distance score indicating how dissimilar the two face images are: the lower the score the more similar the two face images. Ideally, images of the same face should produce low scores, while images of different faces should produce high scores.Every image is compared with every other image, no image is compared with itself and nopair is compared more than once (we assume that the relationship is symmetrical). Once two images have been compared, producing a similarity score, the ground-truth is used to determine if the images are of the same person or different people. In practical tests this information is often encapsulated as part of the image filename (by means of a unique person identifier). Scores are then stored in one of two lists: a list containing scores produced by comparing images of different people and a list containing scores produced by comparing images of the same person. The final acceptance/rejection decision is made by application of a threshold. Any incorrect decision is recorded as either a false acceptance or false rejection. The false rejection rate (FRR) is calculated as the percentage of scores from the same people that were classified as rejections. The false acceptance rate (FAR) is calculated as the percentage of scores from different people that were classified as acceptances.For IndexA = 0 to length(TestSet) For IndexB = IndexA+l to length(TestSet) Score = CompareFaces(TestSet[IndexA], TestSet[IndexB]) If IndexA and IndexB are the same person Append Score to AcceptScoresListElseAppend Score to RejectScoresListFor Threshold = Minimum Score to Maximum Score:FalseAcceptCount, FalseRejectCount = 0For each Score in RejectScoresListIf Score <= ThresholdIncrease FalseAcceptCountFor each Score in AcceptScoresListIf Score > ThresholdIncrease FalseRejectCountF alse AcceptRate = FalseAcceptCount / Length(AcceptScoresList)FalseRej ectRate = FalseRejectCount / length(RejectScoresList)Add plot to error curve at (FalseRejectRate, FalseAcceptRate)These two error rates express the inadequacies of the system when operating at aspecific threshold value. Ideally, both these figures should be zero, but in reality reducing either the FAR or FRR (by altering the threshold value) will inevitably resultin increasing the other. Therefore, in order to describe the full operating range of a particular system, we vary the threshold value through the entire range of scores produced. The application of each threshold value produces an additional FAR, FRR pair, which when plotted on a graph produces the error rate curve shown below.False Acceptance Rate / %Figure 4-5 - Example Error Rate Curve produced by the verification test.The equal error rate (EER) can be seen as the point at which FAR is equal to FRR. This EER value is often used as a single figure representing the general recognition performance of a biometric system and allows for easy visual comparison of multiple methods. However, it is important to note that the EER does not indicate the level of error that would be expected in a real world application. It is unlikely that any real system would use a threshold value such that the percentage of false acceptances were equal to the percentage of false rejections. Secure site access systems would typically set the threshold such that false acceptances were significantly lower than false rejections: unwilling to tolerate intruders at the cost of inconvenient access denials.Surveillance systems on the other hand would require low false rejection rates to successfully identify people in a less controlled environment. Therefore we should bear in mind that a system with a lower EER might not necessarily be the better performer towards the extremes of its operating capability.There is a strong connection between the above graph and the receiver operating characteristic (ROC) curves, also used in such experiments. Both graphs are simply two visualisations of the same results, in that the ROC format uses the True Acceptance Rate(TAR), where TAR = 1.0 - FRR in place of the FRR, effectively flipping the graph vertically. Another visualisation of the verification test results is to display both the FRR and FAR as functions of the threshold value. This presentation format provides a reference to determine the threshold value necessary to achieve a specific FRR and FAR. The EER can be seen as the point where the two curves intersect.Figure 4-6 - Example error rate curve as a function of the score threshold The fluctuation of these error curves due to noise and other errors is dependant on the number of face image comparisons made to generate the data. A small dataset that only allows fbr a small number of comparisons will results in a jagged curve, in which large steps correspond to the influence of a single image on a high proportion of the comparisons made. A typical dataset of 720 images (as used in section 4.2.2) provides 258,840 verification operations, hence a drop of 1% EER represents an additional 2588 correct decisions, whereas the quality of a single image could cause the EER to fluctuate by up to 0.28.422 ResultsAs a simple experiment to test the direct correlation method, we apply the technique described above to a test set of 720 images of 60 different people, taken from the AR Face Database [ 39 ]. Every image is compared with every other image in the test set to produce a likeness score, providing 258,840 verification operations from which to calculate false acceptance rates and false rejection rates. The error curve produced is shown in Figure 4-7.Figure 4-7 - Error rate curve produced by the direct correlation method using no image preprocessing.We see that an EER of 25.1% is produced, meaning that at the EER threshold approximately one quarter of all verification operations carried out resulted in an incorrect classification. Thereare a number of well-known reasons for this poor level of accuracy. Tiny changes in lighting, expression or head orientation cause the location in image space to change dramatically. Images in face space are moved far apart due to these image capture conditions, despite being of the same person's face. The distance between images of different people becomes smaller than the area of face space covered by images of the same person and hence false acceptances and false rejections occur frequently. Other disadvantages include the large amount of storage necessaryfor holding many face images and the intensive processing required for each comparison, making this method unsuitable fbr applications applied to a large database. In section 4.3 we explore the eigenface method, which attempts to address some of these issues.4二维人脸识别4.1功能定位在讨论比较两个人脸图像，我们现在就简要介绍的方法一些在人脸特征的初步调整过程。

数字图像处理外文翻译参考文献

数字图像处理外文翻译参考文献(文档含中英文对照即英文原文和中文翻译)原文：Application Of Digital Image Processing In The MeasurementOf Casting Surface RoughnessAhstract- This paper presents a surface image acquisition system based on digital image processing technology. The image acquired by CCD is pre-processed through the procedure of image editing, image equalization, the image binary conversation and feature parameters extraction to achieve casting surface roughness measurement. The three-dimensional evaluation method is taken to obtain the evaluation parametersand the casting surface roughness based on feature parameters extraction. An automatic detection interface of casting surface roughness based on MA TLAB is compiled which can provide a solid foundation for the online and fast detection of casting surface roughness based on image processing technology.Keywords-casting surface; roughness measurement; image processing; feature parametersⅠ.INTRODUCTIONNowadays the demand for the quality and surface roughness of machining is highly increased, and the machine vision inspection based on image processing has become one of the hotspot of measuring technology in mechanical industry due to their advantages such as non-contact, fast speed, suitable precision, strong ability of anti-interference, etc [1,2]. As there is no laws about the casting surface and the range of roughness is wide, detection parameters just related to highly direction can not meet the current requirements of the development of the photoelectric technology, horizontal spacing or roughness also requires a quantitative representation. Therefore, the three-dimensional evaluation system of the casting surface roughness is established as the goal [3,4], surface roughness measurement based on image processing technology is presented. Image preprocessing is deduced through the image enhancement processing, the image binary conversation. The three-dimensional roughness evaluation based on the feature parameters is performed . An automatic detection interface of casting surface roughness based on MA TLAB is compiled which provides a solid foundation for the online and fast detection of casting surface roughness.II. CASTING SURFACE IMAGE ACQUISITION SYSTEMThe acquisition system is composed of the sample carrier, microscope, CCD camera, image acquisition card and the computer. Sample carrier is used to place tested castings. According to the experimental requirements, we can select a fixed carrier and the sample location can be manually transformed, or select curing specimens and the position of the sampling stage can be changed. Figure 1 shows the whole processing procedure.,Firstly,the detected castings should be placed in the illuminated backgrounds as far as possible, and then through regulating optical lens, setting the CCD camera resolution and exposure time, the pictures collected by CCD are saved to computer memory through the acquisition card. The image preprocessing and feature value extraction on casting surface based on corresponding software are followed. Finally the detecting result is output.III. CASTING SURFACE IMAGE PROCESSINGCasting surface image processing includes image editing, equalization processing, image enhancement and the image binary conversation,etc. The original and clipped images of the measured casting is given in Figure 2. In which a) presents the original image and b) shows the clipped image.A.Image EnhancementImage enhancement is a kind of processing method which can highlight certain image information according to some specific needs and weaken or remove some unwanted informations at the same time[5].In order to obtain more clearly contour of the casting surface equalization processing of the image namely the correction of the image histogram should be pre-processed before image segmentation processing. Figure 3 shows the original grayscale image and equalization processing image and their histograms. As shown in the figure, each gray level of the histogram has substantially the same pixel point and becomes more flat after gray equalization processing. The image appears more clearly after the correction and the contrast of the image is enhanced.Fig.2 Casting surface imageFig.3 Equalization processing imageB. Image SegmentationImage segmentation is the process of pixel classification in essence. It is a very important technology by threshold classification. The optimal threshold is attained through the instmction thresh = graythresh (II). Figure 4 shows the image of the binary conversation. The gray value of the black areas of the Image displays the portion of the contour less than the threshold (0.43137), while the white area shows the gray value greater than the threshold. The shadows and shading emerge in the bright region may be caused by noise or surface depression.Fig4 Binary conversationIV. ROUGHNESS PARAMETER EXTRACTIONIn order to detect the surface roughness, it is necessary to extract feature parameters of roughness. The average histogram and variance are parameters used to characterize the texture size of surface contour. While unit surface's peak area is parameter that can reflect the roughness of horizontal workpiece.And kurtosis parameter can both characterize the roughness of vertical direction and horizontal direction. Therefore, this paper establisheshistogram of the mean and variance, the unit surface's peak area and the steepness as the roughness evaluating parameters of the castings 3D assessment. Image preprocessing and feature extraction interface is compiled based on MATLAB. Figure 5 shows the detection interface of surface roughness. Image preprocessing of the clipped casting can be successfully achieved by this software, which includes image filtering, image enhancement, image segmentation and histogram equalization, and it can also display the extracted evaluation parameters of surface roughness.Fig.5 Automatic roughness measurement interfaceV. CONCLUSIONSThis paper investigates the casting surface roughness measuring method based on digital Image processing technology. The method is composed of image acquisition, image enhancement, the image binary conversation and the extraction of characteristic parameters of roughness casting surface. The interface of image preprocessing and the extraction of roughness evaluation parameters is compiled by MA TLAB which can provide a solid foundation for the online and fast detection of casting surface roughness.REFERENCE[1] Xu Deyan, Lin Zunqi. The optical surface roughness research pro gress and direction[1]. Optical instruments 1996, 18 (1): 32-37.[2] Wang Yujing. Turning surface roughness based on image measurement [D]. Harbin:Harbin University of Science and Technology[3] BRADLEY C. Automated surface roughness measurement[1]. The InternationalJournal of Advanced Manufacturing Technology ,2000,16(9) :668-674.[4] Li Chenggui, Li xing-shan, Qiang XI-FU 3D surface topography measurement method[J]. Aerospace measurement technology, 2000, 20(4): 2-10.[5] Liu He. Digital image processing and application [ M]. China Electric Power Press,2005译文：数字图像处理在铸件表面粗糙度测量中的应用摘要—本文提出了一种表面图像采集基于数字图像处理技术的系统。

图像处理-毕设论文外文翻译(翻译+原文)

英文资料翻译Image processing is not a one step process．We are able to distinguish between several steps which must be performed one after the other until we can extract the data of interest from the observed scene．In this way a hierarchical processing scheme is built up as sketched in Fig．The figure gives an overview of the different phases of image processing.Image processing begins with the capture of an image with a suitable，not necessarily optical，acquisition system．In a technical or scientific application，we may choose to select an appropriate imaging system．Furthermore，we can set up the illumination system，choose the best wavelength range，and select other options to capture the object feature of interest in the best way in an image．Once the image is sensed，it must be brought into a form that can be treated with digital computers.This process is called digitization.With the problems of traffic are more and more serious. Thus Intelligent Transport System (ITS) comes out. The subject of the automatic recognition of license plate is one of the most significant subjects that are improved from the connection of computer vision and pattern recognition. The image imputed to the computer is disposed and analyzed in order to localization the position and recognition the characters on the license plate express these characters in text string form The license plate recognition system (LPSR) has important application in ITS. In LPSR, the first step is for locating the license plate in the captured image which is very important for character recognition. The recognition correction rate of license plate is governed by accurate degree of license plate location. In this paper, several of methods in image manipulation are compared and analyzed, then come out the resolutions for localization of the car plate. The experiences show that the good result has been got with these methods. The methods based on edge map and frequency analysis is used in the process of the localization of the license plate, that is to say, extracting the characteristics of the license plate in the car images after being checked up forthe edge, and then analyzing and processing until the probably area of license plate is extracted.The automated license plate location is a part of the image processing ,it’s also an important part in the intelligent traffic system．It is the key step in the Vehicle License Plate Recognition(LPR).A method for the recognition of images of different backgrounds and different illuminations is proposed in the paper.the upper and lower borders are determined through the gray variation regulation of the character distribution.The left and right borders are determined through the black-white variation of the pixels in every row.The first steps of digital processing may include a number of different operations and are known as image processing．If the sensor has nonlinear characteristics, these need to be corrected．Likewise，brightness and contrast of the image may require improvement．Commonly，too，coordinate transformations are needed to restore geometrical distortions introduced during image formation．Radiometric and geometric corrections are elementary pixel processing operations．It may be necessary to correct known disturbances in the image，for instance caused by a defocused optics，motion blur，errors in the sensor，or errors in the transmission of image signals．We also deal with reconstruction techniques which are required with many indirect imaging techniques such as tomography that deliver no direct image.A whole chain of processing steps is necessary to analyze and identify objects．First，adequate filtering procedures must be applied in order to distinguish the objects of interest from other objects and the background．Essentially，from an image（or several images），one or more feature images are extracted．The basic tools for this task are averaging and edge detection and the analysis of simple neighborhoods and complex patterns known as texture in image processing．An important feature of an object is also its motion．Techniques to detect and determine motion are necessary．Then the object has to be separated from the background．This means that regions of constant features and discontinuities must be identified．This process leads to alabel image．Now that we know the exact geometrical shape of the object，we can extract further information such as the mean gray value，the area，perimeter，and other parameters for the form of the object[3]．These parameters can be used to classify objects．This is an important step in many applications of image processing，as the following examples show：In a satellite image showing an agricultural area，we would like to distinguish fields with different fruits and obtain parameters to estimate their ripeness or to detect damage by parasites．There are many medical applications where the essential problem is to detect pathologi-al changes．A classic example is the analysis of aberrations in chromosomes．Character recognition in printed and handwritten text is another example which has been studied since image processing began and still poses significant difficulties．You hopefully do more，namely try to understand the meaning of what you are reading．This is also the final step of image processing，where one aims to understand the observed scene．We perform this task more or less unconsciously whenever we use our visual system．We recognize people，we can easily distinguish between the image of a scientific lab and that of a living room，and we watch the traffic to cross a street safely．We all do this without knowing how the visual system works．For some times now，image processing and computer-graphics have been treated as two different areas．Knowledge in both areas has increased considerably and more complex problems can now be treated．Computer graphics is striving to achieve photorealistic computer-generated images of three-dimensional scenes，while image processing is trying to reconstruct one from an image actually taken with a camera．In this sense，image processing performs the inverse procedure to that of computer graphics．We start with knowledge of the shape and features of an object—at the bottom of Fig. and work upwards until we get a two-dimensional image．To handle image processing or computer graphics，we basically have to work from the same knowledge．We need to know the interaction between illumination and objects，how a three-dimensional scene is projected onto an image plane，etc．There are still quite a few differences between an image processing and a graphics workstation．But we can envisage that，when the similarities and interrelations between computergraphics and image processing are better understood and the proper hardware is developed，we will see some kind of general-purpose workstation in the future which can handle computer graphics as well as image processing tasks[5]．The advent of multimedia，i. e. ，the integration of text，images，sound，and movies，will further accelerate the unification of computer graphics and image processing.In January 1980 Scientific American published a remarkable image called Plume2，the second of eight volcanic eruptions detected on the Jovian moon by the spacecraft Voyager 1 on 5 March 1979．The picture was a landmark image in interplanetary exploration—the first time an erupting volcano had been seen in space．It was also a triumph for image processing．Satellite imagery and images from interplanetary explorers have until fairly recently been the major users of image processing techniques，where a computer image is numerically manipulated to produce some desired effect-such as making a particular aspect or feature in the image more visible.Image processing has its roots in photo reconnaissance in the Second World War where processing operations were optical and interpretation operations were performed by humans who undertook such tasks as quantifying the effect of bombing raids．With the advent of satellite imagery in the late 1960s，much computer-based work began and the color composite satellite images，sometimes startlingly beautiful, have become part of our visual culture and the perception of our planet．Like computer graphics，it was until recently confined to research laboratories which could afford the expensive image processing computers that could cope with the substantial processing overheads required to process large numbers of high-resolution images．With the advent of cheap powerful computers and image collection devices like digital cameras and scanners，we have seen a migration of image processing techniques into the public domain．Classical image processing techniques are routinely employed bygraphic designers to manipulate photographic and generated imagery，either to correct defects，change color and so on or creatively to transform the entire look of an image by subjecting it to some operation such as edge enhancement.A recent mainstream application of image processing is the compression of images—either for transmission across the Internet or the compression of moving video images in video telephony and video conferencing．Video telephony is one of the current crossover areas that employ both computer graphics and classical image processing techniques to try to achieve very high compression rates．All this is part of an inexorable trend towards the digital representation of images．Indeed that most powerful image form of the twentieth century—the TV image—is also about to be taken into the digital domain．Image processing is characterized by a large number of algorithms that are specific solutions to specific problems．Some are mathematical or context-independent operations that are applied to each and every pixel．For example，we can use Fourier transforms to perform image filtering operations．Others are“algorithmic”—we may use a complicated recursive strategy to find those pixels that constitute the edges in an image．Image processing operations often form part of a computer vision system．The input image may be filtered to highlight or reveal edges prior to a shape detection usually known as low-level operations．In computer graphics filtering operations are used extensively to avoid abasing or sampling artifacts．中文翻译图像处理不是一步就能完成的过程。

ImageProcessing1-Introduction 数字图像处理英文版

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Examples: The Hubble Telescope
Launched in 1990 the Hubble telescope can take images of very distant objects However, an incorrect mirror made many of Hubble’s images useless Image processing techniques were used to fix this
– New reproduction processes based on photographic techniques – Increased number of tones in reproduced images
Images taken from Gonzalez & Woods, Digital Image Processing (2002)
– Image enhancement/restoration – Artistic effects – Medical visualisation – Industrial inspection – Law enforcement – Human computer interfaces
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1970s: Digital image processing begins to be used in medical applications
– 1979: Sir Godfrey N. Hounsfield & Prof. Allan M. Cormack share the Nobel Prize in medicine for the invention of tomography, the technology behind Computerised Axial Tomography (CAT) scans

数字图像处理英文文献翻译参考

…………………………………………………装………………订………………线…………………………………………………………………Hybrid Genetic Algorithm Based Image EnhancementTechnologyMu Dongzhou Department of the Information Engineering XuZhou College of Industrial TechnologyXuZhou, China ****************.cnXu Chao and Ge Hongmei Department of the Information Engineering XuZhou College of Industrial TechnologyXuZhou, China ***************.cn,***************.cnAbstract—in image enhancement, Tubbs proposed a normalized incomplete Beta function to represent several kinds of commonly used non-linear transform functions to do the research on image enhancement. But how to define the coefficients of the Beta function is still a problem. We proposed a Hybrid Genetic Algorithm which combines the Differential Evolution to the Genetic Algorithm in the image enhancement process and utilize the quickly searching ability of the algorithm to carry out the adaptive mutation and searches. Finally we use the Simulation experiment to prove the effectiveness of the method.Keywords- Image enhancement; Hybrid Genetic Algorithm; adaptive enhancementI. INTRODUCTIONIn the image formation, transfer or conversion process, due to other objective factors such as system noise, inadequate or excessive exposure, relative motion and so the impact will get the image often a difference between the original image (referred to as degraded or degraded) Degraded image is usually blurred or after the extraction of information through the machine to reduce or even wrong, it must take some measures for its improvement.Image enhancement technology is proposed in this sense, and the purpose is to improve the image quality. Fuzzy Image Enhancement situation according to the image using a variety of special technical highlights some of the information in the image, reduce or eliminate the irrelevant information, to emphasize the image of the whole or the purpose of local features. Image enhancement method is still no unified theory, image enhancement techniques can be divided into three categories: point operations, and spatial frequency enhancement methods Enhancement Act. This paper presents an automatic adjustment according to the image characteristics of adaptive image enhancement method that called hybrid genetic algorithm. It combines the differential evolution algorithm of adaptive search capabilities, automatically determines the transformation function of the parameter values in order to achieve adaptive image enhancement.…………………………………………………装………………订………………线…………………………………………………………………II. IMAGE ENHANCEMENT TECHNOLOGYImage enhancement refers to some features of the image, such as contour, contrast, emphasis or highlight edges, etc., in order to facilitate detection or further analysis and processing. Enhancements will not increase the information in the image data, but will choose the appropriate features of the expansion of dynamic range, making these features more easily detected or identified, for the detection and treatment follow-up analysis and lay a good foundation.Image enhancement method consists of point operations, spatial filtering, and frequency domain filtering categories. Point operations, including contrast stretching, histogram modeling, and limiting noise and image subtraction techniques. Spatial filter including low-pass filtering, median filtering, high pass filter (image sharpening). Frequency filter including homomorphism filtering, multi-scale multi-resolution image enhancement applied [1].III. DIFFERENTIAL EVOLUTION ALGORITHMDifferential Evolution (DE) was first proposed by Price and Storn, and with other evolutionary algorithms are compared, DE algorithm has a strong spatial search capability, and easy to implement, easy to understand. DE algorithm is a novel search algorithm, it is first in the search space randomly generates the initial population and then calculate the difference between any two members of the vector, and the difference is added to the third member of the vector, by which Method to form a new individual. If you find that the fitness of new individual members better than the original, then replace the original with the formation of individual self.The operation of DE is the same as genetic algorithm, and it conclude mutation, crossover and selection, but the methods are different. We suppose that the group size is P, the vector dimension is D, and we can express the object vector as (1):xi=[xi1，xi2，…，xiD] (i =1,…,P)(1) And the mutation vector can be expressed as (2):()321rrriXXFXV-⨯+=i=1,...,P (2) 1rX,2rX,3rX are three randomly selected individuals from group, and r1≠r2≠r3≠i.F is a range of [0, 2] between the actual type constant factor difference vector is used to control the influence, commonly referred to as scaling factor. Clearly the difference between the vector and the smaller the disturbance also smaller, which means that if groups close to the optimum value, the disturbance will be automatically reduced.DE algorithm selection operation is a "greedy " selection mode, if and only if the new vector ui the fitness of the individual than the target vector is better when the individual xi, ui will be retained to the next group. Otherwise, the target vector xi individuals remain in the original group, once again as the next generation of the parent vector.…………………………………………………装………………订………………线…………………………………………………………………IV. HYBRID GA FOR IMAGE ENHANCEMENT IMAGEenhancement is the foundation to get the fast object detection, so it is necessary to find real-time and good performance algorithm. For the practical requirements of different systems, many algorithms need to determine the parameters and artificial thresholds. Can use a non-complete Beta function, it can completely cover the typical image enhancement transform type, but to determine the Beta function parameters are still many problems to be solved. This section presents a Beta function, since according to the applicable method for image enhancement, adaptive Hybrid genetic algorithm search capabilities, automatically determines the transformation function of the parameter values in order to achieve adaptive image enhancement.The purpose of image enhancement is to improve image quality, which are more prominent features of the specified restore the degraded image details and so on. In the degraded image in a common feature is the contrast lower side usually presents bright, dim or gray concentrated. Low-contrast degraded image can be stretched to achieve a dynamic histogram enhancement, such as gray level change. We use Ixy to illustrate the gray level of point (x, y) which can be expressed by (3).Ixy=f(x, y) (3) where: “f” is a linear or nonline ar function. In general, gray image have four nonlinear translations [6] [7] that can be shown as Figure 1. We use a normalized incomplete Beta function to automatically fit the 4 categories of image enhancement transformation curve. It defines in (4):()()()()10,01,111<<-=---⎰βαβαβαdtttBufu(4) where:()()⎰---=1111,dtttBβαβα(5) For different value of α and β, we can get response curve from (4) and (5).The hybrid GA can make use of the previous section adaptive differential evolution algorithm to search for the best function to determine a value of Beta, and then each pixel grayscale values into the Beta function, the corresponding transformation of Figure 1, resulting in ideal image enhancement. The detail description is follows:Assuming the original image pixel (x, y) of the pixel gray level by the formula (4),denoted byxyi,()Ω∈yx,, here Ω is the image domain. Enhanced image is denoted by Ixy. Firstly, the image gray value normalized into [0, 1] by (6).minmaxminiiiig xyxy--=(6)where:maxi andm ini express the maximum and minimum of image gray relatively.Define the nonlinear transformation function f(u) (0≤u≤1) to transform source image…………………………………………………装………………订………………线…………………………………………………………………Finally, we use the hybrid genetic algorithm to determine the appropriate Beta function f (u) the optimal parameters αand β. Will enhance the image Gxy transformed antinormalized.V. EXPERIMENT AND ANALYSISIn the simulation, we used two different types of gray-scale images degraded; the program performed 50 times, population sizes of 30, evolved 600 times. The results show that the proposed method can very effectively enhance the different types of degraded image.Figure 2, the size of the original image a 320 × 320, it's the contrast to low, and some details of the more obscure, in particular, scarves and other details of the texture is not obvious, visual effects, poor, using the method proposed in this section, to overcome the above some of the issues and get satisfactory image results, as shown in Figure 5 (b) shows, the visual effects have been well improved. From the histogram view, the scope of the distribution of image intensity is more uniform, and the distribution of light and dark gray area is more reasonable. Hybrid genetic algorithm to automatically identify the nonlinear…………………………………………………装………………订………………线…………………………………………………………………transformation of the function curve, and the values obtained before 9.837,5.7912, from the curve can be drawn, it is consistent with Figure 3, c-class, that stretch across the middle region compression transform the region, which were consistent with the histogram, the overall original image low contrast, compression at both ends of the middle region stretching region is consistent with human visual sense, enhanced the effect of significantly improved.Figure 3, the size of the original image a 320 × 256, the overall intensity is low, the use of the method proposed in this section are the images b, we can see the ground, chairs and clothes and other details of the resolution and contrast than the original image has Improved significantly, the original image gray distribution concentrated in the lower region, and the enhanced image of the gray uniform, gray before and after transformation and nonlinear transformation of basic graph 3 (a) the same class, namely, the image Dim region stretching, and the values were 5.9409,9.5704, nonlinear transformation of images degraded type inference is correct, the enhanced visual effect and good robustness enhancement.Difficult to assess the quality of image enhancement, image is still no common evaluation criteria, common peak signal to noise ratio (PSNR) evaluation in terms of line, but the peak signal to noise ratio does not reflect the human visual system error. Therefore, we use marginal protection index and contrast increase index to evaluate the experimental results.Edgel Protection Index (EPI) is defined as follows:…………………………………………………装………………订………………线…………………………………………………………………(7)Contrast Increase Index (CII) is defined as follows:minmaxminmax,GGGGCCCEOD+-==(8)In figure 4, we compared with the Wavelet Transform based algorithm and get the evaluate number in TABLE I.Figure 4 (a, c) show the original image and the differential evolution algorithm for enhanced results can be seen from the enhanced contrast markedly improved, clearer image details, edge feature more prominent. b, c shows the wavelet-based hybrid genetic algorithm-based Comparison of Image Enhancement: wavelet-based enhancement method to enhance image detail out some of the image visual effect is an improvement over the original image, but the enhancement is not obvious; and Hybrid genetic algorithm based on adaptive transform image enhancement effect is very good, image details, texture, clarity is enhanced compared with the results based on wavelet transform has greatly improved the image of the post-analytical processing helpful. Experimental enhancement experiment using wavelet transform "sym4" wavelet, enhanced differential evolution algorithm experiment, the parameters and the values were 5.9409,9.5704. For a 256 × 256 size image transform based on adaptive hybrid genetic algorithm in Matlab 7.0 image enhancement software, the computing time is about 2 seconds, operation is very fast. From TABLE I, objective evaluation criteria can be seen, both the edge of the protection index, or to enhance the contrast index, based on adaptive hybrid genetic algorithm compared to traditional methods based on wavelet transform has a larger increase, which is from This section describes the objective advantages of the method. From above analysis, we can see…………………………………………………装………………订………………线…………………………………………………………………that this method.From above analysis, we can see that this method can be useful and effective.VI. CONCLUSIONIn this paper, to maintain the integrity of the perspective image information, the use of Hybrid genetic algorithm for image enhancement, can be seen from the experimental results, based on the Hybrid genetic algorithm for image enhancement method has obvious effect. Compared with other evolutionary algorithms, hybrid genetic algorithm outstanding performance of the algorithm, it is simple, robust and rapid convergence is almost optimal solution can be found in each run, while the hybrid genetic algorithm is only a few parameters need to be set and the same set of parameters can be used in many different problems. Using the Hybrid genetic algorithm quick search capability for a given test image adaptive mutation, search, to finalize the transformation function from the best parameter values. And the exhaustive method compared to a significant reduction in the time to ask and solve the computing complexity. Therefore, the proposed image enhancement method has some practical value.REFERENCES[1] HE Bin et al., Visual C++ Digital Image Processing [M], Posts & Telecom Press,2001,4:473～477[2] Storn R, Price K. Differential Evolution—a Simple and Efficient Adaptive Scheme forGlobal Optimization over Continuous Space[R]. International Computer Science Institute, Berlaey, 1995.[3] Tubbs J D. A note on parametric image enhancement [J].Pattern Recognition.1997,30(6):617-621.[4] TANG Ming, MA Song De, XIAO Jing. Enhancing Far Infrared Image Sequences withModel Based Adaptive Filtering [J] . CHINESE JOURNAL OF COMPUTERS, 2000, 23(8):893-896.[5] ZHOU Ji Liu, LV Hang, Image Enhancement Based on A New Genetic Algorithm [J].Chinese Journal of Computers, 2001, 24(9):959-964.[6] LI Yun, LIU Xuecheng. On Algorithm of Image Constract Enhancement Based onWavelet Transformation [J]. Computer Applications and Software, 2008,8.[7] XIE Mei-hua, WANG Zheng-ming, The Partial Differential Equation Method for ImageResolution Enhancement [J]. Journal of Remote Sensing, 2005，9(6):673-679.…………………………………………………装………………订………………线…………………………………………………………………基于混合遗传算法的图像增强技术Mu Dongzhou 徐州工业职业技术学院信息工程系 XuZhou, China****************.cnXu Chao and Ge Hongmei 徐州工业职业技术学院信息工程系 XuZhou,********************.cn,***************.cn摘要—在图像增强之中，塔布斯提出了归一化不完全β函数表示常用的几种使用的非线性变换函数对图像进行研究增强。

数字图像检测中英文对照外文翻译文献

中英文对照外文翻译(文档含英文原文和中文翻译)Edge detection in noisy images by neuro-fuzzyprocessing通过神经模糊处理的噪声图像边缘检测AbstractA novel neuro-fuzzy (NF) operator for edge detection in digital images corrupted by impulse noise is presented. The proposed operator is constructed by combining a desired number of NF subdetectors with a postprocessor. Each NF subdetector in the structure evaluates a different pixel neighborhood relation. Hence, the number of NF subdetectors in the structure may be varied to obtain the desired edge detection performance. Internal parameters of the NF subdetectors are adaptively optimized by training by using simple artificial training images. The performance of the proposed edge detector is evaluated on different test images and compared with popular edge detectors from the literature. Simulation results indicate that the proposed NF operator outperforms competing edge detectors and offers superior performance in edge detection in digital images corrupted by impulse noise.Keywords: Neuro-fuzzy systems; Image processing; Edge detection摘要针对被脉冲信号干扰的数字图像进行边缘检测，提出了一种新型的NF边缘检测器，它是由一定数量的NF子探测器与一个后处理器组成。

计算机图像图形外文翻译外文文献英文文献图像分割

原文出处Digital Image Processing 2/E图像分割前一章的资料使我们所研究的图像处理方法开始发生了转变。

从输人输出均为图像的处理方法转变为输人为图像而输出为从这些图像中提取出来的属性的处理方法〔这方面在1.1节中定义过)。

图像分割是这一方向的另一主要步骤。

分割将图像细分为构成它的子区域或对象。

分割的程度取决于要解决的问题。

就是说当感兴趣的对象已经被分离出来时就停止分割。

例如，在电子元件的自动检测方面，我们关注的是分析产品的图像，检测是否存在特定的异常状态，比如，缺失的元件或断裂的连接线路。

超过识别这此元件所需的分割是没有意义的。

异常图像的分割是图像处理中最困难的任务之一。

精确的分割决定着计算分析过程的成败。

因此，应该特别的关注分割的稳定性。

在某些情况下，比如工业检测应用，至少有可能对环境进行适度控制的检测。

有经验的图像处理系统设计师总是将相当大的注意力放在这类可能性上。

在其他应用方面，比如自动目标采集，系统设计者无法对环境进行控制。

所以，通常的方法是将注意力集中于传感器类型的选择上，这样可以增强获取所关注对象的能力，从而减少图像无关细节的影响。

一个很好的例子就是，军方利用红外线图像发现有很强热信号的目标，比如移动中的装备和部队。

图像分割算法一般是基于亮度值的不连续性和相似性两个基本特性之一。

第一类性质的应用途径是基于亮度的不连续变化分割图像，比如图像的边缘。

第二类的主要应用途径是依据事先制定的准则将图像分割为相似的区域，门限处理、区域生长、区域分离和聚合都是这类方法的实例。

本章中，我们将对刚刚提到的两类特性各讨论一些方法。

我们先从适合于检测灰度级的不连续性的方法展开，如点、线和边缘。

特别是边缘检测近年来已经成为分割算法的主题。

除了边缘检测本身，我们还会讨论一些连接边缘线段和把边缘“组装”为边界的方法。

关于边缘检测的讨论将在介绍了各种门限处理技术之后进行。

门限处理也是一种人们普遍关注的用于分割处理的基础性方法，特别是在速度因素占重要地位的应用中。

数字图像处理(Mathematical Morphology)

膨胀
A
B AB
离散情况下的明克夫斯基和（膨胀）
小结
1、膨胀可以实现图像缝隙的连接；
2、腐蚀可以去除小颗粒噪声或毛刺；
3、多种组合，实现开、闭、击中、击不中； 4、典型的非线性滤波，滤波效果可交互控制；
5、模板设计与算法设计
膨胀、腐蚀的组合滤波效果
应用
边界提取
骨架抽取极限腐蚀 Top-hat变换流域变换灰度形态变换
腐蚀的性质
1、如果原点在结构元素的内部，则腐蚀后的图像为原图像的一个子集，即腐蚀具有收缩图像的作用，也就是可以去除比模板小的噪声； 2、如果原点不在结构元素的内部，则腐蚀后的图像可能不在原图像的内部，反而可能具有填充图像内孔洞的作用。
A
A
B
B
原点在结构元素内部时的腐蚀
原点不在结构元素内部时的腐蚀

Applications
– Filtering – Segmentation – Coding & Compression – Object detection – Computer vision
Question
What is Mathematical Morphology ?
A Commercial Answer
A B 集合A被集合B―腐蚀”，表示为
AB {x : B x A}
其中A为输入图像，B为结构元素腐蚀的结果由将B平移x，但仍然包含在A内的所有x点组成。如果将B看作模板，则由在平移过程中，所有可以填入A 内部的模板的原点组成。腐蚀还有几种常用表示：E(A,B)，ERODE(A,B)
– Basic Morphology Operators

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数字图像处理外文翻译参考文献

人脸识别 面部 数字图像处理相关 中英对照 外文文献翻译 毕业设计论文 高质量人工翻译 原文带出处

数字图像处理外文翻译参考文献

图像处理-毕设论文外文翻译(翻译+原文)

ImageProcessing1-Introduction 数字图像处理 英文版

数字图像处理英文文献翻译参考

数字图像检测中英文对照外文翻译文献

计算机图像图形外文翻译外文文献英文文献图像分割

数字图像处理(Mathematical Morphology)

人脸识别面部数字图像处理相关中英对照外文文献翻译毕业设计论文高质量人工翻译原文带出处

ImageProcessing1-Introduction 数字图像处理英文版