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附录2：外文翻译Robust Analysis of Feature Spaces: Color ImageSegmentationAbstractA general technique for the recovery of significant image features is presented. The technique is based on the mean shift algorithm, a simple nonparametric procedure for estimating density gradients. Drawbacks of the current methods (including robust clustering) are avoided. Feature space of any nature can be processed, and as an example, color image segmentation is discussed. The segmentation is completely autonomous, only its class is chosen by the user. Thus, the same program can produce a high quality edge image, or provide, by extracting all the significant colors, a preprocessor for content-based query systems. A 512 512 color image is analyzed in less than 10 seconds on a standard workstation. Gray level images are handled as color images having only the lightness coordinate.Keywords:robust pattern analysis, low-level vision, content-based indexing1 IntroductionFeature space analysis is a widely used tool for solving low-level image understanding tasks. Given an image, feature vectors are extracted from local neighborhoods and mapped into the space spanned by their components. Significant features in the image then correspond to high density regions in this space. Feature space analysis is the procedure of recovering the centers of the high density regions, i.e., the representations of the significant image features. Histogram based techniques, Hough transform are examples of the approach.When the number of distinct feature vectors is large, the size of the feature space is reduced by grouping nearby vectors into a single cell.A discretized feature space is called an accumulator. Whenever the size of the accumulator cell is not adequate for the data, serious artifacts can appear. The problem was extensively studied in the context of the Hough transform, e.g.. Thus, for satisfactory results a feature space should have continuous coordinate system. The content of a continuous feature space can be modeled as a sample from a multivariate, multimodal probability distribution. Note that for real images the number of modes can be very large, of the order of tens.The highest density regions correspond to clusters centered on the modes of the underlying probability distribution. Traditional clustering techniques, can be used for feature space analysis but they are reliable only if the number of clusters is small and known a priori. Estimating the number of clusters from the data is computationally expensive and not guaranteed to produce satisfactory result.A much too often used assumption is that the individual clusters obey multivariate normal distributions, i.e., the feature space can be modeled as a mixture of Gaussians. The parameters of the mixture are then estimated by minimizing an error criterion. For example, a large class ofthresholding algorithms are based on the Gaussian mixture model of the histogram, e.g.. However, there is no theoretical evidence that an extracted normal cluster necessarily corresponds to a significant image feature. On the contrary, a strong artifact cluster may appear when several features are mapped into partially overlapping regions.Nonparametric density estimation avoids the use of the normality assumption. The two families of methods, Parzen window, and k-nearest neighbors, both require additional input information (type of the kernel, number of neighbors). This information must be provided by the user, and for multimodal distributions it is difficult to guess the optimal setting.Nevertheless, a reliable general technique for feature space analysis can be developed using a simple nonparametric density estimation algorithm. In this paper we propose such a technique whose robust behavior is superior to methods employing robust estimators from statistics.2 Requirements for RobustnessEstimation of a cluster center is called in statistics the multivariate location problem. To be robust, an estimator must tolerate a percentage of outliers, i.e., data points not obeying the underlying distribution of the cluster. Numerous robust techniques were proposed, and in computer vision the most widely used is the minimum volume ellipsoid (MVE) estimator proposed by Rousseeuw.The MVE estimator is affine equivariant (an affine transformation of the input is passed on to the estimate) and has high breakdown point (tolerates up to half the data being outliers). The estimator finds the center of the highest density region by searching for the minimal volume ellipsoid containing at least h data points. The multivariate location estimate is the center of this ellipsoid. To avoid combinatorial explosion a probabilistic search is employed. Let the dimension of the data be p.A small number of (p+1) tuple of points are randomly chosen. For each (p+1) tuple the mean vector and covariance matrix are computed, defining an ellipsoid. The ellipsoid is inated to include h points, and the one having the minimum volume provides the MVE estimate.Based on MVE, a robust clustering technique with applications in computer vision was proposed in. The data is analyzed under several \resolutions" by applying the MVE estimator repeatedly with h values representing fixed percentages of the data points. The best cluster then corresponds to the h value yielding the highest density inside the minimum volume ellipsoid. The cluster is removed from the feature space, and the whole procedure is repeated till the space is not empty. The robustness of MVE should ensure that each cluster is associated with only one mode of the underlying distribution. The number of significant clusters is not needed a priori.The robust clustering method was successfully employed for the analysis of a large variety of feature spaces, but was found to become less reliable once the number of modes exceeded ten. This is mainly due to the normality assumption embedded into the method. The ellipsoid defining a cluster can be also viewed as the high confidence region of a multivariate normal distribution. Arbitrary feature spaces are not mixtures of Gaussians and constraining the shape of the removed clusters to be elliptical can introduce serious artifacts. The effect of these artifacts propagates as more and more clusters are removed. Furthermore, the estimated covariance matrices are not reliable since are based on only p + 1 points. Subsequent post processing based on all the points declared inliers cannot fully compensate for an initial error.To be able to correctly recover a large number of significant features, the problem of feature space analysis must be solved in context. In image understanding tasks the data to be analyzed originates in the image domain. That is, the feature vectors satisfy additional, spatial constraints. While these constraints are indeed used in the current techniques, their role is mostly limited to compensating for feature allocation errors made during the independent analysis of the feature space. To be robust the feature space analysis must fully exploit the image domain information.As a consequence of the increased role of image domain information the burden on the feature space analysis can be reduced. First all the significant features are extracted, and only after then are the clusters containing the instances of these features recovered. The latter procedure uses image domain information and avoids the normality assumption.Significant features correspond to high density regions and to locate these regions a search window must be employed. The number of parameters defining the shape and size of the window should be minimal, and therefore whenever it is possible the feature space should be isotropic. A space is isotropic if the distance between two points is independent on the location of the point pair. The most widely used isotropic space is the Euclidean space, where a sphere, having only one parameter (its radius) can be employed as search window. The isotropy requirement determines the mapping from the image domain to the feature space. If the isotropy condition cannot be satisfied, a Mahalanobis metric should be defined from the statement of the task.We conclude that robust feature space analysis requires a reliable procedure for the detection of high density regions. Such a procedure is presented in the next section.3 Mean Shift AlgorithmA simple, nonparametric technique for estimation of the density gradient was proposed in 1975 by Fukunaga and Hostetler. The idea was recently generalized by Cheng.Assume, for the moment, that the probability density function p(x) of the p-dimensional feature vectors x is unimodal. This condition is forS of radius r, sake of clarity only, later will be removed. A sphereXcentered on x contains the feature vectors y such thatr x y ≤-. The expected value of the vector x y z -=, given x and X S is[]()()()()()dy S y p y p x y dy S y p x y S z E X X S X S X X ⎰⎰∈-=-==μ （1） If X S is sufficiently small we can approximate()()X S X V x p S y p =∈，where p S r c V X ⋅= （2）is the volume of the sphere. The first order approximation of p(y) is()()()()x p x y x p y p T∇-+= （3） where ()x p ∇ is the gradient of the probability density function in x. Then()()()()⎰∇--=X X S S Tdy x p x p V x y x y μ （4） since the first term vanishes. The value of the integral is()()x p x p p r ∇+=22μ （5） or[]()()x p x p p r x S x x E X ∇+=-∈22 （6） Thus, the mean shift vector, the vector of difference between the local mean and the center of the window, is proportional to the gradient of the probability density at x. The proportionality factor is reciprocal to p(x). This is beneficial when the highest density region of the probability density function is sought. Such region corresponds to large p(x) and small ()x p ∇, i.e., to small mean shifts. On the other hand, low density regions correspond to large mean shifts (amplified also by small p(x) values). The shifts are always in the direction of the probability density maximum, the mode. At the mode the mean shift is close to zero. This property can be exploited in a simple, adaptive steepest ascent algorithm.Mean Shift Algorithm1. Choose the radius r of the search window.2. Choose the initial location of the window.3. Compute the mean shift vector and translate the search window by that amount.4. Repeat till convergence.To illustrate the ability of the mean shift algorithm, 200 data points were generated from two normal distributions, both having unit variance. The first hundred points belonged to a zero-mean distribution, the second hundred to a distribution having mean 3.5. The data is shown as a histogram in Figure 1. It should be emphasized that the feature space is processed as an ordered one-dimensional sequence of points, i.e., it is continuous. The mean shift algorithm starts from the location of the mode detected by the one-dimensional MVE mode detector, i.e., the center of the shortest rectangular window containing half the data points. Since the data is bimodal with nearby modes, the mode estimator fails and returns a location in the trough. The starting point is marked by the cross at the top of Figure 1.Figure 1: An example of the mean shift algorithm.In this synthetic data example no a priori information is available about the analysis window. Its size was taken equal to that returned by the MVE estimator, 3.2828. Other, more adaptive strategies for setting the search window size can also be defined.Table 1: Evolution of Mean Shift AlgorithmIn Table 1 the initial values and the final location,shown with a star at the top of Figure 1, are given.The mean shift algorithm is the tool needed for feature space analysis. The unimodality condition can be relaxed by randomly choosing the initial location of the search window. The algorithm then converges to the closest high density region. The outline of a general procedure is given below.Feature Space Analysis1. Map the image domain into the feature space.2. Define an adequate number of search windows at random locations in the space.3. Find the high density region centers by applying the mean shift algorithm to each window.4. Validate the extracted centers with image domain constraints to provide the feature palette.5. Allocate, using image domain information, all the feature vectors to the feature palette.The procedure is very general and applicable to any feature space. In the next section we describe a color image segmentation technique developed based on this outline.4 Color Image SegmentationImage segmentation, partioning the image into homogeneous regions, is a challenging task. The richness of visual information makes bottom-up, solely image driven approaches always prone to errors. To be reliable, the current systems must be large and incorporate numerous ad-hoc procedures, e.g.. The paradigms of gray level image segmentation (pixel-based, area-based, edge-based) are also used for color images. In addition, the physics-based methods take into account information about the image formation processes as well. See, for example, the reviews. The proposed segmentation technique does not consider the physical processes, it uses only the given image, i.e., a set of RGB vectors. Nevertheless,can be easily extended to incorporate supplementary information about the input. As homogeneity criterion color similarity is used.Since perfect segmentation cannot be achieved without a top-down, knowledge driven component, a bottom-up segmentation technique should ·only provide the input into the next stage where the task is accomplished using a priori knowledge about its goal; and·eliminate, as much as possible, the dependence on user set parameter values.Segmentation resolution is the most general parameter characterizing a segmentation technique. Whilethis parameter has a continuous scale, three important classes can be distinguished.Undersegmentation corresponds to the lowest resolution. Homogeneity is defined with a large tolerance margin and only the most significant colors are retained for the feature palette. The region boundaries in a correctly undersegmented image are the dominant edges in the image.Oversegmentation corresponds to intermediate resolution. The feature palette is rich enough that the image is broken into many small regions from which any sought information can be assembled under knowledge control. Oversegmentation is the recommended class when the goal of the task is object recognition.Quantization corresponds to the highest resolution.The feature palette contains all the important colors in the image. This segmentation class became important with the spread of image databases, e.g.. The full palette, possibly together with the underlying spatial structure, is essential for content-based queries.The proposed color segmentation technique operates in any of the these three classes. The user only chooses the desired class, the specific operating conditions are derived automatically by the program.Images are usually stored and displayed in the RGB space. However, to ensure the isotropy of the feature space, a uniform color space with the perceived color differences measured by Euclidean distances should be used. We have chosen the***v u L space, whose coordinates are related to the RGB values by nonlinear transformations. The daylight standard 65D was used as reference illuminant. The chromatic information is carried by *u and *v , while the lightness coordinate *L can be regarded as the relative brightness. Psychophysical experiments show that ***v u L space may not be perfectly isotropic, however, it was found satisfactory for image understanding applications. The image capture/display operations also introduce deviations which are most often neglected.The steps of color image segmentation are presented below. The acronyms ID and FS stand for image domain and feature space respectively. All feature space computations are performed in the ***v u L space.1. [FS] Definition of the segmentation parameters.The user only indicates the desired class of segmentation. The class definition is translated into three parameters·the radius of the search window, r;·the smallest number of elements required for a significant color, min N ;·the smallest number of contiguous pixels required for a significant image region, con N .The size of the search window determines the resolution of the segmentation, smaller values corresponding to higher resolutions. The subjective (perceptual) definition of a homogeneous region seems to depend on the “visual activity” in the image. Within the same segmentation class an image containing large homogeneous regions should be analyzed at higher resolution than an image with many textured areas. The simplest measure of the “visual activity” can be derived from the global covariance matrix. The square root of its trace,σ, is related to the power of the signal(image). The radius r is taken proportional to σ. The rules defining the three segmentation class parameters are given in Table 2. These rules were used in the segmentation of a large variety images, ranging from simple blood cells to complex indoor and outdoorscenes.When the goal of the task is well defined and/or all the images are of the same type, the parameters can be fine tuned.Table 2: Segmentation Class Parameters2. [ID+FS] Definition of the search window.The initial location of the search window in the feature space is randomly chosen. To ensure that the search starts close to a high density region several location candidates are examined. The random sampling is performed in the image domain and a few, M = 25, pixels are chosen. For each pixel, the mean of its 3⨯3 neighborhood is computed and mapped into the feature space. If the neighborhood belongs to a larger homogeneous region, with high probability the location of the search window will be as wanted. To further increase this probability, the window containing the highest density of feature vectors is selected from the M candidates.3. [FS] Mean shift algorithm.To locate the closest mode the mean shift algorithm is applied to the selected search window. Convergence is declared when the magnitude of the shift becomes less than 0.1.4. [ID+FS] Removal of the detected feature.The pixels yielding feature vectors inside the search window at its final location are discarded from both domains. Additionally, their 8-connected neighbors in the image domain are also removed independent of the feature vector value. These neighbors can have “strange” colors due to the image formation process and their removal cleans the background of the feature space. Since all pixels are reallocated in Step 7, possible errors will be corrected.5. [ID+FS] Iterations.Repeat Steps 2 to 4, till the number of feature vectors in the selectedN.search window no longer exceedsmin6. [ID] Determining the initial feature palette.In the feature space a significant color must be based on minimumN vectors. Similarly, to declare a color significant in the image minN pixels of that color should belong to a connected domain more thanmincomponent. From the extracted colors only those are retained for theinitial feature palette which yield at least one connected component inN. The neighbors removed at Step 4 are the image of size larger thanminalso considered when defining the connected components Note that the N which is used only at the post processing stage. threshold is notcon7. [ID+FS] Determining the final feature palette.The initial feature palette provides the colors allowed whensegmenting the image. If the palette is not rich enough the segmentationresolution was not chosen correctly and should be increased to the nextclass. All the pixel are reallocated based on this palette. First, thepixels yielding feature vectors inside the search windows at their finallocation are considered. These pixels are allocated to the color of thewindow center without taking into account image domain information. Thewindows are then inflated to double volume (their radius is multiplied with p32). The newly incorporated pixels are retained only if they have at least one neighbor which was already allocated to that color. The mean of the feature vectors mapped into the same color is the value retained for the final palette. At the end of the allocation procedure a small number of pixels can remain unclassified. These pixels are allocated to the closest color in the final feature palette.8. [ID+FS] Postprocessing.This step depends on the goal of the task. The simplest procedure is the removal from the image of all small connected components of size less N.These pixels are allocated to the majority color in their 3⨯thancon3 neighborhood, or in the case of a tie to the closest color in the feature space.In Figure 2 the house image containing 9603 different colors is shown. The segmentation results for the three classes and the region boundaries are given in Figure 5a-f. Note that undersegmentation yields a good edge map, while in the quantization class the original image is closely reproduced with only 37 colors. A second example using the oversegmentation class is shown in Figure 3. Note the details on the fuselage.5 DiscussionThe simplicity of the basic computational module, the mean shift algorithm, enables the feature space analysis to be accomplished very fast. From a 512⨯512 pixels image a palette of 10-20 features can be extracted in less than 10 seconds on a Ultra SPARC 1 workstation. To achieve such a speed the implementation was optimized and whenever possible, the feature space (containing fewer distinct elements than the image domain) was used for array scanning; lookup tables were employed instead of frequently repeated computations; direct addressing instead of nested pointers; fixed point arithmetic instead of floating point calculations; partial computation of the Euclidean distances, etc.The analysis of the feature space is completely autonomous, due to the extensive use of image domain information. All the examples in this paper, and dozens more not shown here, were processed using the parameter values given in Table 2. Recently Zhu and Yuille described a segmentation technique incorporating complex global optimization methods(snakes, minimum description length) with sensitive parameters and thresholds. To segment a color image over a hundred iterations were needed. When the images used in were processed with the technique described in this paper, the same quality results were obtained unsupervised and in less than a second. The new technique can be used un modified for segmenting gray levelimages, which are handled as color images with only the*L coordinates. In Figure 6 an example is shown.The result of segmentation can be further refined by local processing in the image domain. For example, robust analysis of the pixels in a large connected component yields the inlier/outlier dichotomy which then can be used to recover discarded fine details.In conclusion, we have presented a general technique for feature space analysis with applications in many low-level vision tasks like thresholding, edge detection, segmentation. The nature of the feature space is not restricted, currently we are working on applying the technique to range image segmentation, Hough transform and optical flow decomposition.255⨯ pixels, 9603 colors.Figure 2: The house image, 192（a）（b）Figure 3: Color image segmentation example.512⨯ pixels, 77041 colors. (b)Oversegmentation: (a)Original image, 51221/21 colors.（a ） （b ） Figure 4: Performance comparison.(a) Original image, 261116 pixels, 200 colors. (b) Undersegmentation:5/4 colors. Region boundaries.（a）（b）（c）（d）（e）（f）Figure 5: The three segmentation classes for the house image. The right column shows the region boundaries.(a)(b) Undersegmentation. Number of colors extracted initially and in thefeature palette: 8/8.(c)(d) Oversegmentation: 24/19 colors. (e)(f) Quantization: 49/37 colors.（a）（b）（c）Figure 6: Gray level image segmentation example. (a)Original image,256 pixels.256(b) Undersegmenta-tion: 5 gray levels. (c) Region boundaries.特征空间稳健性分析：彩色图像分割摘要本文提出了一种恢复显著图像特征的普遍技术。

毕业设计（论文）外文文献翻译文献、资料中文题目： 1.使用阈值技术的图像分割2.最大类间方差算法的图像分割综述文献、资料英文题目：文献、资料来源：文献、资料发表（出版）日期：院（部）：专业：计算机科学与技术班级：姓名：学号：指导教师：翻译日期： 2017.02.14毕业设计（论文）题目基于遗传算法的自动图像分割软件开发翻译（1）题目Image Segmentation by Using ThresholdTechniques翻译（2）题目A Review on Otsu Image Segmentation Algorithm使用阈值技术的图像分割 1摘要本文试图通过5阈值法作为平均法，P-tile算法，直方图相关技术（HDT），边缘最大化技术（EMT）和可视化技术进行了分割图像技术的研究，彼此比较从而选择合的阈值分割图像的最佳技术。

这些技术适用于三个卫星图像选择作为阈值分割图像的基本猜测。

关键词：图像分割，阈值，自动阈值1 引言分割算法是基于不连续性和相似性这两个基本属性之一的强度值。

第一类是基于在强度的突然变化，如在图像的边缘进行分区的图像。

第二类是根据预定义标准基于分割的图像转换成类似的区域。

直方图阈值的方法属于这一类。

本文研究第二类（阈值技术）在这种情况下，通过这项课题可以给予这些研究简要介绍。

阈分割技术可分为三个不同的类：首先局部技术基于像素和它们临近地区的局部性质。

其次采用全局技术分割图像可以获得图像的全局信息（通过使用图像直方图，例如;全局纹理属性）。

并且拆分，合并，生长技术，为了获得良好的分割效果同时使用的同质化和几何近似的概念。

最后的图像分割，在图像分析的领域中，常用于将像素划分成区域，以确定一个图像的组成[1][2]。

他们提出了一种二维（2-D）的直方图基于多分辨率分析（MRA）的自适应阈值的方法，降低了计算的二维直方图的复杂而提高了多分辨率阈值法的搜索精度。

这样的方法源于通过灰度级和灵活性的空间相关性的多分辨率阈值分割方法中的阈值的寻找以及效率由二维直方图阈值分割方法所取得的非凡分割效果。

中英文对照外文翻译文献(文档含英文原文和中文翻译)Elastic image matchingAbstractOne fundamental problem in image recognition is to establish the resemblance of two images. This can be done by searching the best pixel to pixel mapping taking into account monotonicity and continuity constraints. We show that this problem is NP-complete by reduction from 3-SAT, thus giving evidence that the known exponential time algorithms are justified, but approximation algorithms or simplifications are necessary.Keywords: Elastic image matching; Two-dimensional warping; NP-completeness 1. IntroductionIn image recognition, a common problem is to match two given images, e.g. when comparing an observed image to given references. In that pro-cess, elastic image matching, two-dimensional (2D-)warping (Uchida and Sakoe, 1998) or similar types of invariant methods (Keysers et al., 2000) can be used. For this purpose, we can define cost functions depending on the distortion introduced in the matching andsearch for the best matching with respect to a given cost function. In this paper, we show that it is an algorithmically hard problem to decide whether a matching between two images exists with costs below a given threshold. We show that the problem image matching is NP-complete by means of a reduction from 3-SAT, which is a common method of demonstrating a problem to be intrinsically hard (Garey and Johnson, 1979). This result shows the inherent computational difficulties in this type of image comparison, while interestingly the same problem is solvable for 1D sequences in polynomial time, e.g. the dynamic time warping problem in speech recognition (see e.g. Ney et al., 1992). This has the following implications: researchers who are interested in an exact solution to this problem cannot hope to find a polynomial time algorithm, unless P=NP. Furthermore, one can conclude that exponential time algorithms as presented and extended by Uchida and Sakoe (1998, 1999a,b, 2000a,b) may be justified for some image matching applications. On the other hand this shows that those interested in faster algorithms––e.g. for pattern recognition purposes––are right in searching for sub-optimal solutions. One method to do this is the restriction to local optimizations or linear approximations of global transformations as presented in (Keysers et al., 2000). Another possibility is to use heuristic approaches like simulated annealing or genetic algorithms to find an approximate solution. Furthermore, methods like beam search are promising candidates, as these are used successfully in speech recognition, although linguistic decoding is also an NP-complete problem (Casacuberta and de la Higuera, 1999). 2. Image matchingAmong the varieties of matching algorithms,we choose the one presented by Uchida and Sakoe(1998) as a starting point to formalize the problem image matching. Let the images be given as(without loss of generality) square grids of size M×M with gray values (respectively node labels)from a finite alphabet &={1，…，G}. To define thed：&×&→N , problem, two distance functions are needed,one acting on gray valuesg measuring the match in gray values, and one acting on displacement differences :Z×Z→N , measuring the distortion introduced by t he matching. For these distance ddfunctions we assume that they are monotonous functions (computable in polynomial time) of the commonly used squared Euclid-ean distance, i.ed g (g 1,g 2)=f 1(||g 1-g 2||²)and d d (z)=f 2(||z||²) monotonously increasing. Now we call the following optimization problem the image matching problem (let µ={1，…M} ).Instance: The pair( A ; B ) of two images A and B of size M×M .Solution: A mapping function f :µ×µ→µ×µ.Measure:c （A,B,f ）=),(),(j i f ij g B Ad ∑μμ⨯∈),(j i+∑⨯-⋅⋅⋅∈+-+μ}1,{1,),()))0,1(),(())0,1(),(((M j i d j i f j i f dμ⨯-⋅⋅⋅∈}1,{1,),(M j i +∑⋅⋅⋅⨯∈+-+1}-M ,{1,),()))1,0(),(())1,0(),(((μj i d j i f j i f d 1}-M ,{1,),(⋅⋅⋅⨯∈μj iGoal:min f c(A,B,f).In other words, the problem is to find the mapping from A onto B that minimizes the distance between the mapped gray values together with a measure for the distortion introduced by the mapping. Here, the distortion is measured by the deviation from the identity mapping in the two dimensions. The identity mapping fulfills f(i,j)=(i,j),and therefore ，f((i,j)+(x,y))=f(i,j)+(x,y)The corresponding decision problem is fixed by the followingQuestion:Given an instance of image matching and a cost c′, does there exist a ma pping f such that c(A,B,f)≤c′?In the definition of the problem some care must be taken concerning the distance functions. For example, if either one of the distance functions is a constant function, the problem is clearly in P (for d g constant, the minimum is given by the identity mapping and for d d constant, the minimum can be determined by sorting all possible matching for each pixel by gray value cost and mapping to one of the pixels with minimum cost). But these special cases are not those we are concerned with in image matching in general.We choose the matching problem of Uchida and Sakoe (1998) to complete the definition of the problem. Here, the mapping functions are restricted by continuity and monotonicity constraints: the deviations from the identity mapping may locally be at most one pixel (i.e. limited to the eight-neighborhood with squared Euclidean distance less than or equal to 2). This can be formalized in this approach bychoosing the functions f1,f2as e.g.f 1=id，f2(x)=step(x):=⎩⎨⎧.2,)10(,2,0>≤⋅xGxMM3. Reduction from 3-SAT3-SAT is a very well-known NP-complete problem (Garey and Johnson, 1979), where 3-SAT is defined as follows:Instance: Collection of clauses C={C1，···,CK} on a set of variables X={x1， (x)L}such that each ckconsists of 3 literals for k=1，···K .Each literal is a variable or the negation of a variable.Question:Is there a truth assignment for X which satisfies each clause ck, k=1，···K ?The dependency graph D（Ф）corresponding to an instance Ф of 3-SAT is defined to be the bipartite graph whose independent sets are formed by the set of clauses Cand the set of variables X .Two vert ices ck and x1are adjacent iff ckinvolvesx 1or-xL.Given any 3-SAT formula U, we show how to construct in polynomial time anequivalent image matching problem l（Ф）=（A(Ф),B(Ф)）; . The two images of l （Ф）are similar according to the cost function （i.e.f:c(A(Ф),B(Ф),f）≤0) iff the formulaФ is satisfiable. We perform the reduction from 3-SAT using the following steps:• From the formula Ф we construct the dependency graph D（Ф）.• The dependency graph D（Ф）is drawn in the plane.• The drawing of D（Ф）is refined to depict the logical behaviour of Ф , yielding two images（A(Ф),B(Ф)）.For this, we use three types of components: one component to represent variables of Ф , one component to represent clauses of Ф, and components which act as interfaces between the former two types. Before we give the formal reduction, we introduce these components.3.1. Basic componentsFor the reduction from 3-SAT we need five components from which we will construct the in-stances for image matching , given a Boolean formula in 3-DNF,respectively its graph. The five components are the building blocks needed for the graph drawing and will be introduced in the following, namely the representations of connectors,crossings, variables, and clauses. The connectors represent the edges and have two varieties, straight connectors and corner connectors. Each of the components consists of two parts, one for image A and one for image B , where blank pixels are considered to be of the‘background ’color.We will depict possible mappings in the following using arrows indicating the direction of displacement (where displacements within the eight-neighborhood of a pixel are the only cases considered). Blank squares represent mapping to the respective counterpart in the second image.For example, the following displacements of neighboring pixels can be used with zero cost:On the other hand, the following displacements result in costs greater than zero:Fig. 1 shows the first component, the straight connector component, which consists of a line of two different interchanging colors,here denoted by the two symbols◇and□. Given that the outside pixels are mapped to their respe ctive counterparts and the connector is continued infinitely, there are two possible ways in which the colored pixels can be mapped, namely to the left (i.e. f(2,j)=(2,j-1)) or to the right (i.e. f(2,j)=(2,j+1)),where the background pixels have different possibilities for the mapping, not influencing the main property of the connector. This property, which justifies the name ‘connector ’, is the following: It is not possible to find a mapping, which yields zero cost where the relative displacements of the connector pixels are not equal, i.e. one always has f(2,j)-(2,j)=f(2,j')-(2,j'),which can easily be observed by induction over j'.That is, given an initial displacement of one pixel (which will be ±1 in this context), the remaining end of the connector has the same displacement if overall costs of the mapping are zero. Given this property and the direction of a connector, which we define to be directed from variable to clause, wecan define the state of the connector as carrying the‘true’truth value, if the displacement is 1 pixel in the direction of the connector and as carrying the‘false’ truth value, if the displacement is -1 pixel in the direction of the connector. This property then ensures that the truth value transmitted by the connector cannot change at mappings of zero cost.Image A image Bmapping 1 mapping 2Fig. 1. The straight connector component with two possible zero cost mappings.For drawing of arbitrary graphs, clearly one also needs corners,which are represented in Fig. 2.By considering all possible displacements which guarantee overall cost zero, one can observe that the corner component also ensures the basic connector property. For example, consider the first depicted mapping, which has zero cost. On the other hand, the second mapping shows, that it is not possible to construct a zero cost mapping with both connectors‘leaving’the component. In that case, the pixel at the position marked‘? ’either has a conflict (that i s, introduces a cost greater than zero in the criterion function because of mapping mismatch) with the pixel above or to the right of it,if the same color is to be met and otherwise, a cost in the gray value mismatch term is introduced.image A image Bmapping 1 mapping 2Fig. 2. The corner connector component and two example mappings.Fig. 3 shows the variable component, in this case with two positive (to the left) and one negated output (to the right) leaving the component as connectors. Here, a fourth color is used, denoted by ·.This component has two possible mappings for thecolored pixels with zero cost, which map the vertical component of the source image to the left or the right vertical component in the target image, respectively. (In both cases the second vertical element in the target image is not a target of the mapping.) This ensures±1 pixel relative displacements at the entry to the connectors. This property again can be deducted by regarding all possible mappings of the two images.The property that follows (which is necessary for the use as variable) is that all zero cost mappings ensure that all positive connectors carry the same truth value,which is the opposite of the truth value for all the negated connectors. It is easy to see from this example how variable components for arbitrary numbers of positive and negated outputs can be constructed.image A image BImage C image DFig. 3. The variable component with two positive and one negated output and two possible mappings (for true and false truth value).Fig. 4 shows the most complex of the components, the clause component. This component consists of two parts. The first part is the horizontal connector with a 'bend' in it to the right.This part has the property that cost zero mappings are possible for all truth values of x and y with the exception of two 'false' values. This two input disjunction，can be extended to a three input dis-junction using the part in the lower left. If the z connector carries a 'false' truth value, this part can only be mapped one pixel downwards at zero cost.In that case the junction pixel (the fourth pixel in the third row) cannot be mapped upwards at zero cost and the 'two input clause' behaves as de-scribed above. On the other hand, if the z connector carries a 'true' truth value, this part can only be mapped one pixel upwards at zero cost,and the junction pixel can be mapped upwards,thus allowing both x and y to carry a 'false' truth value in a zero cost mapping. Thus there exists a zero cost mapping of the clause component iff at least one of the input connectors carries a truth value.image Aimage B mapping 1(true,true,false)mapping 2 (false,false,true,)Fig. 4. The clause component with three incoming connectors x, y , z and zero cost mappings forthe two cases(true,true,false)and (false, false, true).The described components are already sufficient to prove NP-completeness by reduction from planar 3-SAT (which is an NP-complete sub-problem of 3-SAT where the additional constraints on the instances is that the dependency graph is planar),but in order to derive a reduction from 3-SAT, we also include the possibility of crossing connectors.Fig. 5 shows the connector crossing, whose basic property is to allow zero cost mappings if the truth–values are consistently propagated. This is assured by a color change of the vertical connector and a 'flexible' middle part, which can be mapped to four different positions depending on the truth value distribution.image Aimage Bzero cost mappingFig. 5. The connector crossing component and one zero cost mapping.3.2. ReductionUsing the previously introduced components, we can now perform the reduction from 3-SAT to image matching .Proof of the claim that the image matching problem is NP-complete:Clearly, the image matching problem is in NP since, given a mapping f and two images A and B ,the computation of c（A,B,f）can be done in polynomial time. To prove NP-hardness, we construct a reduction from the 3-SAT problem. Given an instance of 3-SAT we construct two images A and B , for which a mapping of cost zero exists iff all the clauses can be satisfied.Given the dependency graph D ,we construct an embedding of the graph into a 2D pixel grid, placing the vertices on a large enough distance from each other (say100（K+L）² ).This can be done using well-known methods from graph drawing (see e.g.di Battista et al.,1999).From this image of the graph D we construct the two images A and B , using the components described above.Each vertex belonging to a variable is replaced with the respective parts of the variable component, having a number of leaving connectors equal to the number of incident edges under consideration of the positive or negative use in the respective clause. Each vertex belonging to a clause is replaced by the respective clause component,and each crossing of edges is replaced by the respective crossing component. Finally, all the edges are replaced with connectors and corner connectors, and the remaining pixels inside the rectangular hull of the construction are set to the background gray value. Clearly, the placement of the components can be done in such a way that all the components are at a large enough distance from each other, where the background pixels act as an 'insulation' against mapping of pixels, which do not belong to the same component. It can be easily seen, that the size of the constructed images is polynomial with respect to the number of vertices and edges of D and thus polynomial in the size of the instance of 3-SAT, at most in the order （K+L）².Furthermore, it can obviously be constructed in polynomial time, as the corresponding graph drawing algorithms are polynomial.Let there exist a truth assignment to the variables x1，…，xL, which satisfies allthe clauses c1，…，cK. We construct a mapping f , that satisfies c（f,A,B）=0 asfollows.For all pixels (i, j ) belonging to variable component l with A（i，j）not of the background color,set f（i，j）=（i，j-1）if xlis assigned the truth value 'true' , set f（i，j）=（i，j+1）, otherwise. For the remaining pixels of the variable component set A（i，j）=B（i，j），if f（i，j）=（i，j）, otherwise choose f（i，j）from{(i,j+1),(i+1,j+1),(i-1,j+1)}for xl'false' respectively from {(i,j-1),(i+1,j-1),(i-1,j-1)}for xl'true ',such that A（i，j）=B（f（i，j））. This assignment is always possible and has zero cost, as can be easily verified.For the pixels（i，j）belonging to (corner) connector components,the mapping function can only be extended in one way without the introduction of nonzero cost,starting from the connection with the variable component. This is ensured by thebasic connector property. By choosing f （i ，j ）=（i,j ）for all pixels of background color, we obtain a valid extension for the connectors. For the connector crossing components the extension is straight forward, although here ––as in the variable mapping ––some care must be taken with the assign ment of the background value pixels, but a zero cost assignment is always possible using the same scheme as presented for the variable mapping.It remains to be shown that the clause components can be mapped at zero cost, if at least one of the input connectors x , y , z carries a ' true' truth value.For a proof we regard alls even possibilities and construct a mapping for each case. In thedescription of the clause component it was already argued that this is possible,and due to space limitations we omit the formalization of the argument here.Finally, for all the pixels （i ，j ）not belonging to any of the components, we set f （i ，j ）=（i ，j ）thus arriving at a mapping function which has c （f ，A ，B ）=0。

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。

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。

(文档含英文原文和中文翻译)中英文对照外文翻译一种在线图像编码识别系统的设计摘要：本文介绍了在线图像编码字符识别系统的设计与实现过程，对其中重点环节进行了分析与研究，给出了主要环节问题的解决方法，在识别算法上，结合模板匹配与特征识别，提出了基于特征加权的模板匹配算法，该算法对提高字符识别率提到了较好的作用。

关键词：图像处理;模式识别;特征加权;软件设计0引言图像编码字符识别的研究目前仍是国内外一个重点研究课题，它具有广泛的应用背景，比如车牌号码自动识别、邮政编码的自动识别、试卷自动阅读、报表自动处理等，由于这种在线图像编码字符的识别都具有一些共性，本文结合在线轮胎编码字符识别系统的设计，对一般图像编码字符识别系统进行了阐述，对关键环节进行了研究与分析，该方法对其它在线图像编码字符系统的开发具有一定指导意义。

1在线图像编码识别系统流程在线图像编码字符识别系统主要包括数字图像的采集、存储、图像预处理、编码图像提取、编码特征提取、编码识别和后续处理等一些环节，其流程图如图1所示。

图1 在线图像编码字符识别系统流程图在线轮胎图像编码字符识别系统要求对通过生产流水线上每一个轮胎采集含有轮胎编码的图像，然后通过对图像的处理，提取出轮胎编码特征，采用合适的识别算法将每一位编码字符进行识别。

由于轮胎编码字符在轮胎上有一定变形，且摄像角度不同，得到的编码图像差异也很大，规律性差，所以编码图像的预处理和识别算法的选取显得尤为重要。

2图像采集与存储在线编码图像通常使用数码摄像机、数码照相机、数码摄像头等设备采集并输入计算机进行处理，本系统采用QuickCamPro4000数码摄像头采集轮胎编码图像,直接按JPG格式存储。

编码图像一般都要先转成BMP图像格式，因为BMP格式己经成为PC领域事实上的标准——几乎所有为Windows操作系统设计的图像处理软件都支持这种格式的图像。

BMP是Windows的原始位图格式，它可以用于保存任意类型的位图数据，可以支持所有的屏幕分辨率和Windows所支持的颜色组合。

…………………………………………………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。

中英文对照外文翻译文献(文档含英文原文和中文翻译)译文：基于局部二值模式多分辨率的灰度和旋转不变性的纹理分类摘要：本文描述了理论上非常简单但非常有效的，基于局部二值模式的、样图的非参数识别和原型分类的，多分辨率的灰度和旋转不变性的纹理分类方法。

此方法是基于结合某种均衡局部二值模式，是局部图像纹理的基本特性，并且已经证明生成的直方图是非常有效的纹理特征。

我们获得一个一般灰度和旋转不变的算子，可表达检测有角空间和空间结构的任意量子化的均衡模式，并提出了结合多种算子的多分辨率分析方法。

根据定义，该算子在图像灰度发生单一变化时具有不变性，所以所提出的方法在灰度发生变化时是非常强健的。

另一个优点是计算简单，算子在小邻域内或同一查找表内只要几个操作就可实现。

在旋转不变性的实际问题中得到了良好的实验结果,与来自其他的旋转角度的样品一起以一个特别的旋转角度试验而且测试得到分类, 证明了基于简单旋转的发生统计学的不变性二值模式的分辨是可以达成。

这些算子表示局部图像纹理的空间结构的又一特色是，由结合所表示的局部图像纹理的差别的旋转不变量不一致方法，其性能可得到进一步的改良。

这些直角的措施共同证明了这是旋转不变性纹理分析的非常有力的工具。

关键词：非参数的，纹理分析，Outex ，Brodatz ，分类，直方图，对比度2 灰度和旋转不变性的局部二值模式我们通过定义单色纹理图像的一个局部邻域的纹理T ，如 P （P>1）个象素点的灰度级联合分布，来描述灰度和旋转不变性算子：01(,,)c P T t g g g -= （1）其中，g c 为局部邻域中心像素点的灰度值，g p （p=0，1…P-1）为半径R(R>0)的圆形邻域内对称的空间象素点集的灰度值。

图1如果g c 的坐标是（0,0），那么g p 的坐标为(cos sin(2/),(2/))R R p P p P ππ-。

图1举例说明了圆形对称邻域集内各种不同的（P,R ）。

图像切割—基于图的图像切割（Graph-BasedImageSegmentation）图像切割—基于图的图像切割（Graph-Based Image Segmentation）Reference:Efficient Graph-Based Image Segmentation,IJCV 2004,MIT最后⼀个暑假了，不打算开疆辟⼟了。

战略中⼼转移到品味经典。

计划把图像切割和⽬标追踪的经典算法都看⼀看。

再记些笔记。

Graph-Based Segmentation 是经典的图像切割算法，作者Felzenszwalb也是提出算法的⼤⽜。

该算法是基于图的贪⼼聚类算法，实现简单。

速度⽐較快，精度也还⾏。

只是。

眼下直接⽤它做切割的应该⽐較少，毕竟是99年的跨世纪元⽼，可是⾮常多算法⽤它作垫脚⽯。

⽐⽅Object Propose的开⼭之作《Segmentation as Selective Search for Object Recognition》就⽤它来产⽣过切割（oversegmentation）。

还有的语义切割(senmatic segmentation )算法⽤它来产⽣超像素（superpixels）详细忘记了……图的基本概念由于该算法是将照⽚⽤加权图抽象化表⽰，所以补充图的⼀些基本概念。

图是由顶点集（vertices）和边集（edges）组成，表⽰为。

顶点，在本⽂中即为单个的像素点。

连接⼀对顶点的边具有权重，本⽂中的意义为顶点之间的不相似度，所⽤的是⽆向图。

树：特殊的图。

图中随意两个顶点，都有路径相连接，可是没有回路。

如上图中加粗的边所连接⽽成的图。

假设看成⼀团乱连的珠⼦，仅仅保留树中的珠⼦和连线。

那么随便选个珠⼦，都能把这棵树中全部的珠⼦都提起来。

假设，i和h这条边也保留下来。

那么顶点h,i,c,f,g就构成了⼀个回路。

最⼩⽣成树（MST, ）：特殊的树。

给定须要连接的顶点，选择边权之和最⼩的树。

…………………………………………………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。

图像分割图像预处理中英文对照外文翻译文献中英文对照外文翻译一种在线图像编码识别系统的设计摘要：本文介绍了在线图像编码字符识别系统的设计与实现过程，对其中重点环节进行了分析与研究，给出了主要环节问题的解决方法，在识别算法上，结合模板匹配与特征识别，提出了基于特征加权的模板匹配算法，该算法对提高字符识别率提到了较好的作用。

关键词：图像处理;模式识别;特征加权;软件设计0引言图像编码字符识别的研究目前仍是国内外一个重点研究课题，它具有广泛的应用背景，比如车牌号码自动识别、邮政编码的自动识别、试卷自动阅读、报表自动处理等，由于这种在线图像编码字符的识别都具有一些共性，本文结合在线轮胎编码字符识别系统的设计，对一般图像编码字符识别系统进行了阐述，对关键环节进行了研究与分析，该方法对其它在线图像编码字符系统的开发具有一定指导意义。

1在线图像编码识别系统流程在线图像编码字符识别系统主要包括数字图像的采集、存储、图像预处理、编码图像提取、编码特征提取、编码识别和后续处理等一些环节，其流程图如图1所示。

图1 在线图像编码字符识别系统流程图在线轮胎图像编码字符识别系统要求对通过生产流水线上每一个轮胎采集含有轮胎编码的图像，然后通过对图像的处理，提取出轮胎编码特征，采用合适的识别算法将每一位编码字符进行识别。

由于轮胎编码字符在轮胎上有一定变形，且摄像角度不同，得到的编码图像差异也很大，规律性差，所以编码图像的预处理和识别算法的选取显得尤为重要。

2图像采集与存储在线编码图像通常使用数码摄像机、数码照相机、数码摄像头等设备采集并输入计算机进行处理，本系统采用QuickCamPro4000数码摄像头采集轮胎编码图像,直接按JPG格式存储。

编码图像一般都要先转成BMP图像格式，因为BMP格式己经成为PC领域事实上的标准——几乎所有为Windows操作系统设计的图像处理软件都支持这种格式的图像。

BMP是Windows的原始位图格式，它可以用于保存任意类型的位图数据，可以支持所有的屏幕分辨率和Windows所支持的颜色组合。

规范化切割和图像分割摘要：为解决在视觉上的感知分组的问题，我们提出了一个新的方法。

我们目的是提取图像的总体印象，而不是只集中于局部特征和图像数据的一致性。

我们把图像分割看成一个图形的划分问题，并且提出一个新的分割图形的全球标准，规范化切割。

这一标准衡量了不同组之间的总差异和总相似。

我们发现基于广义特征值问题的一个高效计算技术可以用于优化标准。

我们已经将这种方法应用于静态图像和运动序列，发现结果是令人鼓舞的。

1简介近75年前，韦特海默推出的“格式塔”的方法奠定了感知分组和视觉感知组织的重要性。

我的目的是，分组问题可以通过考虑图（1）所示点的集合而更加明确。

通常人类观察者在这个图中会看到四个对象，一个圆环和内部的一团点以及右侧两个松散的点团。

然而这并不是唯一的分割情况。

有些人认为有三个对象，可以将右侧的两个认为是一个哑铃状的物体。

或者只有两个对象，右侧是一个哑铃状的物体，左侧是一个类似结构的圆形星系。

如果一个人倒行逆施，他可以认为事实上每一个点是一个不同的对象。

这似乎是一个人为的例子，但每一次图像分割都会面临一个相似的问题—将一个图像的区域D划分成子集Di会有许多可能的划分方式（包括极端的将每一个像素认为是一个单独的实体）。

我们怎样挑选“最正确”的呢？我们相信贝叶斯的观点是合适的，即一个人想要在较早的世界知识背景下找到最合理的解释。

当然，困难在于具体说明较早的世界知识—一些低层次的，例如亮度，颜色，质地或运行的一致性，但是关于物体对称或对象模型的中高层次的知识是同等重要的。

这些表明基于低层次线索的图像分割不能够也不应该旨在产生一个完整的最终的正确的分割。

目标应该是利用低层次的亮度，颜色，质地，或运动属性的一致性继续的提出分层分区。

中高层次的知识可以用于确认这些分组或者选择更深的关注。

这种关注可能会导致更进一步的再分割或分组。

关键点是图像分割是从大的图像向下进行，而不是像画家首先标示出主要区域，然后再填充细节。

用一种快速模糊C 均值算法对磁共振脑部图像进行分割摘要：这篇论文提出了一种新的模糊控制算法的磁共振脑图像分割。

从标准FCM[ 1 ]及其偏差校正版本BCFCM[ 2 ]算法，后者分裂的主要步骤，并引入新的因素 ，所需数量计算是大大减少。

该算法提供良好质量脑图像分割的一个非常快速的方法，这使他成为一个支持虚拟人脑内窥镜很好的工具。

1.引言标准模糊C 均值算法提供了一种脑图像分割，但它的操作没有过滤，因此图像质量仍然很差。

加强版bcfcm [ 2 ]介绍过滤内周期性的优化问题，从而导致更好的图像质量，但操作时间很慢。

本文的主要目的是减少在分割的过程中的数额计算量，提供一个高速优质的磁共振脑图像分割。

2.方法论标准FCM 算法，由Bezdek 等人在[1]有介绍，第k 个数据样本k x 。

k=1..n ，利用目标函数: J B =2)(11∑∑==-c i Nk i v k x ik p u （1）i v 代表第i 类中心，iku 代表第i 类中样本k 的隶属度，p 是加权指数。

通过定义，对任意k ,我们有11ci u ik ==∑ 。

为了把目标函数最小化，应指定高像素数据，其强度是位于接近原型值的特定集群。

Ahmed 等人。

提出了引入一个术语修改的原始目标函数，允许标记的像素是由标签在其邻域[ 2]。

这种效应作为一种正归化，和偏见解决走向分段均匀标记。

这证明在被盐和胡椒噪声损坏的图像分割是有用的。

修改目标函数为J A =∑∑∑===∂-+-ci Nk k Nr k N i v r k x ik pu i v k x ik p u 111]2),(2)([ （2）其中rk x,代表k x 像素的邻域，kN 代表第k 个像素的邻域数目 ，α参数 代表控制强度的邻近效应。

在下面，我们将介绍一些修改算法。

磁共振脑图像栈 约200片，而在其代表大矩阵的像素。

一套磁共振脑图像片包含一千万（lo7）像素。

像素的强度通常是编码8bit 的分辨率，即，，每个像素有 只有256种可能的强度等级。

A Threshold Selection Method from Gray-Level Histograms[1][1]Otsu N, A threshold selection method from gray-level histogram. IEEE Transactions on System，Man，and Cybemetics，SMC-8，1978：62-66．一种由灰度直方图选取阈值的方法摘要介绍了一种对于画面分割自动阈值选择的非参数和无监督的方法。

最佳阈值由判别标准选择，即最大化通过灰度级所得到的类的方差。

这个过程很简单，是利用了灰度直方图0阶和第1阶的累积。

这是简单的方法扩展到多阈值的问题。

几种实验结果呈现也支持了方法的有效性。

一.简介选择灰度充分的阈值，从图片的背景中提取对象对于图像处理非常重要。

在这方面已经提出了多种技术。

在理想的情况下，直方图具有分别表示对象和背景的能力，两个峰之间有很深的明显的谷，使得阈值可以选择这个谷底。

然而，对于大多数实际图片，它常常难以精确地检测谷底，特别是在这种情况下，当谷是平的和广泛的，具有噪声充满时，或者当两个峰是在高度极其不等，通常不产生可追踪的谷。

已经出现了，为了克服这些困难，提出的一些技术。

它们是，例如，谷锐化技术[2]，这个技术限制了直方图与（拉普拉斯或梯度）的衍生物大于绝对值的像素，并且描述了绘制差分直方图方法[3]，选择灰度级的阈值与差的最大值。

这些利用在原始图象有关的信息的相邻像素（或边缘），修改直方图以便使其成为阈值是有用的。

另一类方法与参数方法的灰度直方图直接相关。

例如，该直方图在最小二乘意义上与高斯分布的总和近似，应用了统计决策程序 [4]。

然而，这种方法需要相当繁琐，有时不稳定的计算。

此外，在许多情况下，高斯分布与真实模型的近似值较小。

在任何情况下，没有一个阈值的评估标准能够对大多数的迄今所提出的方法进行评价。

这意味着，它可能是派生的最佳阈值方法来建立一个适当的标准，从更全面的角度评估阈值的“好与坏”的正确方法。

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。

外文文献资料翻译：李睿钦指导老师：刘文军Medical image registration with partial dataSenthil Periaswamy，Hany FaridThe goal of image registration is to find a transformation that aligns one image to another. Medical image registration has emerged from this broad area of research as a particularly active field. This activity is due in part to the many clinical applications including diagnosis, longitudinal studies, and surgical planning, and to the need for registration across different imaging modalities (e.g., MRI, CT, PET, X-ray, etc.). Medical image registration, however, still presents many challenges. Several notable difficulties are (1) the transformation between images can vary widely and be highly non-rigid in nature; (2) images acquired from different modalities may differ significantly in overall appearance and resolution; (3) there may not be a one-to-one correspondence between the images (missing/partial data); and (4) each imaging modality introduces its own unique challenges, making it difficult to develop a single generic registration algorithm.In estimating the transformation that aligns two images we must choose: (1) to estimate the transformation between a small number of extracted features, or between the complete unprocessed intensity images; (2) a model that describes the geometric transformation; (3) whether to and how to explicitly model intensity changes; (4) an error metric that incorporates the previous three choices; and (5) a minimization technique for minimizing the error metric, yielding the desired transformation.Feature-based approaches extract a (typically small) number of corresponding landmarks or features between the pair of images to be registered. The overall transformation is estimated from these features. Common features include corresponding points, edges, contours or surfaces. These features may be specified manually or extracted automatically. Fiducial markers may also be used as features;these markers are usually selected to be visible in different modalities. Feature-based approaches have the advantage of greatly reducing computational complexity. Depending on the feature extraction process, these approaches may also be more robust to intensity variations that arise during, for example, cross modality registration. Also, features may be chosen to help reduce sensor noise. These approaches can be, however, highly sensitive to the accuracy of the feature extraction. Intensity-based approaches, on the other hand, estimate the transformation between the entire intensity images. Such an approach is typically more computationally demanding, but avoids the difficulties of a feature extraction stage.Independent of the choice of a feature- or intensity-based technique, a model describing the geometric transform is required. A common and straightforward choice is a model that embodies a single global transformation. The problem of estimating a global translation and rotation parameter has been studied in detail, and a closed form solution was proposed by Schonemann. Other closed-form solutions include methods based on singular value decomposition (SVD), eigenvalue-eigenvector decomposition and unit quaternions. One idea for a global transformation model is to use polynomials. For example, a zeroth-order polynomial limits the transformation to simple translations, a first-order polynomial allows for an affine transformation, and, of course, higher-order polynomials can be employed yielding progressively more flexible transformations. For example, the registration package Automated Image Registration (AIR) can employ (as an option) a fifth-order polynomial consisting of 168 parameters (for 3-D registration). The global approach has the advantage that the model consists of a relatively small number of parameters to be estimated, and the global nature of the model ensures a consistent transformation across the entire image. The disadvantage of this approach is that estimation of higher-order polynomials can lead to an unstable transformation, especially near the image boundaries. In addition, a relatively small and local perturbation can cause disproportionate and unpredictable changes in the overall transformation. An alternative to these global approaches are techniques that model the global transformation as a piecewise collection of local transformations. For example, the transformation between each local region may bemodeled with a low-order polynomial, and global consistency is enforced via some form of a smoothness constraint. The advantage of such an approach is that it is capable of modeling highly nonlinear transformations without the numerical instability of high-order global models. The disadvantage is one of computational inefficiency due to the significantly larger number of model parameters that need to be estimated, and the need to guarantee global consistency. Low-order polynomials are, of course, only one of many possible local models that may be employed. Other local models include B-splines, thin-plate splines, and a multitude of related techniques. The package Statistical Parametric Mapping (SPM) uses the low-frequency discrete cosine basis functions, where a bending-energy function is used to ensure global consistency. Physics-based techniques that compute a local geometric transform include those based on the Navier–Stokes equilibrium equations for linear elastici and those based on viscous fluid approaches.Under certain conditions a purely geometric transformation is sufficient to model the transformation between a pair of images. Under many real-world conditions, however, the images undergo changes in both geometry and intensity (e.g., brightness and contrast). Many registration techniques attempt to remove these intensity differences with a pre-processing stage, such as histogram matching or homomorphic filtering. The issues involved with modeling intensity differences are similar to those involved in choosing a geometric model. Because the simultaneous estimation of geometric and intensity changes can be difficult, few techniques build explicit models of intensity differences. A few notable exceptions include AIR, in which global intensity differences are modeled with a single multiplicative contrast term, and SPM in which local intensity differences are modeled with a basis function approach.Having decided upon a transformation model, the task of estimating the model parameters begins. As a first step, an error function in the model parameters must be chosen. This error function should embody some notion of what is meant for a pair of images to be registered. Perhaps the most common choice is a mean square error (MSE), defined as the mean of the square of the differences (in either feature distance or intensity) between the pair of images. This metric is easy to compute and oftenaffords simple minimization techniques. A variation of this metric is the unnormalized correlation coefficient applicable to intensity-based techniques. This error metric is defined as the sum of the point-wise products of the image intensities, and can be efficiently computed using Fourier techniques. A disadvantage of these error metrics is that images that would qualitatively be considered to be in good registration may still have large errors due to, for example, intensity variations, or slight misalignments. Another error metric (included in AIR) is the ratio of image uniformity (RIU) defined as the normalized standard deviation of the ratio of image intensities. Such a metric is invariant to overall intensity scale differences, but typically leads to nonlinear minimization schemes. Mutual information, entropy and the Pearson product moment cross correlation are just a few examples of other possible error functions. Such error metrics are often adopted to deal with the lack of an explicit model of intensity transformations .In the final step of registration, the chosen error function is minimized yielding the desired model parameters. In the most straightforward case, least-squares estimation is used when the error function is linear in the unknown model parameters. This closed-form solution is attractive as it avoids the pitfalls of iterative minimization schemes such as gradient-descent or simulated annealing. Such nonlinear minimization schemes are, however, necessary due to an often nonlinear error function. A reasonable compromise between these approaches is to begin with a linear error function, solve using least-squares, and use this solution as a starting point for a nonlinear minimization.译文：部分信息的医学图像配准Senthil Periaswamy，Hany Farid图像配准的目的是找到一种能把一副图像对准另外一副图像的变换算法。