毕业论文外文翻译-图像分割
数字图像处理外文翻译参考文献
数字图像处理外文翻译参考文献(文档含中英文对照即英文原文和中文翻译)原文：Application Of Digital Image Processing In The MeasurementOf Casting Surface RoughnessAhstract- This paper presents a surface image acquisition system based on digital image processing technology. The image acquired by CCD is pre-processed through the procedure of image editing, image equalization, the image binary conversation and feature parameters extraction to achieve casting surface roughness measurement. The three-dimensional evaluation method is taken to obtain the evaluation parametersand the casting surface roughness based on feature parameters extraction. An automatic detection interface of casting surface roughness based on MA TLAB is compiled which can provide a solid foundation for the online and fast detection of casting surface roughness based on image processing technology.Keywords-casting surface; roughness measurement; image processing; feature parametersⅠ.INTRODUCTIONNowadays the demand for the quality and surface roughness of machining is highly increased, and the machine vision inspection based on image processing has become one of the hotspot of measuring technology in mechanical industry due to their advantages such as non-contact, fast speed, suitable precision, strong ability of anti-interference, etc [1,2]. As there is no laws about the casting surface and the range of roughness is wide, detection parameters just related to highly direction can not meet the current requirements of the development of the photoelectric technology, horizontal spacing or roughness also requires a quantitative representation. Therefore, the three-dimensional evaluation system of the casting surface roughness is established as the goal [3,4], surface roughness measurement based on image processing technology is presented. Image preprocessing is deduced through the image enhancement processing, the image binary conversation. The three-dimensional roughness evaluation based on the feature parameters is performed . An automatic detection interface of casting surface roughness based on MA TLAB is compiled which provides a solid foundation for the online and fast detection of casting surface roughness.II. CASTING SURFACE IMAGE ACQUISITION SYSTEMThe acquisition system is composed of the sample carrier, microscope, CCD camera, image acquisition card and the computer. Sample carrier is used to place tested castings. According to the experimental requirements, we can select a fixed carrier and the sample location can be manually transformed, or select curing specimens and the position of the sampling stage can be changed. Figure 1 shows the whole processing procedure.,Firstly,the detected castings should be placed in the illuminated backgrounds as far as possible, and then through regulating optical lens, setting the CCD camera resolution and exposure time, the pictures collected by CCD are saved to computer memory through the acquisition card. The image preprocessing and feature value extraction on casting surface based on corresponding software are followed. Finally the detecting result is output.III. CASTING SURFACE IMAGE PROCESSINGCasting surface image processing includes image editing, equalization processing, image enhancement and the image binary conversation,etc. The original and clipped images of the measured casting is given in Figure 2. In which a) presents the original image and b) shows the clipped image.A.Image EnhancementImage enhancement is a kind of processing method which can highlight certain image information according to some specific needs and weaken or remove some unwanted informations at the same time[5].In order to obtain more clearly contour of the casting surface equalization processing of the image namely the correction of the image histogram should be pre-processed before image segmentation processing. Figure 3 shows the original grayscale image and equalization processing image and their histograms. As shown in the figure, each gray level of the histogram has substantially the same pixel point and becomes more flat after gray equalization processing. The image appears more clearly after the correction and the contrast of the image is enhanced.Fig.2 Casting surface imageFig.3 Equalization processing imageB. Image SegmentationImage segmentation is the process of pixel classification in essence. It is a very important technology by threshold classification. The optimal threshold is attained through the instmction thresh = graythresh (II). Figure 4 shows the image of the binary conversation. The gray value of the black areas of the Image displays the portion of the contour less than the threshold (0.43137), while the white area shows the gray value greater than the threshold. The shadows and shading emerge in the bright region may be caused by noise or surface depression.Fig4 Binary conversationIV. ROUGHNESS PARAMETER EXTRACTIONIn order to detect the surface roughness, it is necessary to extract feature parameters of roughness. The average histogram and variance are parameters used to characterize the texture size of surface contour. While unit surface's peak area is parameter that can reflect the roughness of horizontal workpiece.And kurtosis parameter can both characterize the roughness of vertical direction and horizontal direction. Therefore, this paper establisheshistogram of the mean and variance, the unit surface's peak area and the steepness as the roughness evaluating parameters of the castings 3D assessment. Image preprocessing and feature extraction interface is compiled based on MATLAB. Figure 5 shows the detection interface of surface roughness. Image preprocessing of the clipped casting can be successfully achieved by this software, which includes image filtering, image enhancement, image segmentation and histogram equalization, and it can also display the extracted evaluation parameters of surface roughness.Fig.5 Automatic roughness measurement interfaceV. CONCLUSIONSThis paper investigates the casting surface roughness measuring method based on digital Image processing technology. The method is composed of image acquisition, image enhancement, the image binary conversation and the extraction of characteristic parameters of roughness casting surface. The interface of image preprocessing and the extraction of roughness evaluation parameters is compiled by MA TLAB which can provide a solid foundation for the online and fast detection of casting surface roughness.REFERENCE[1] Xu Deyan, Lin Zunqi. The optical surface roughness research pro gress and direction[1]. Optical instruments 1996, 18 (1): 32-37.[2] Wang Yujing. Turning surface roughness based on image measurement [D]. Harbin:Harbin University of Science and Technology[3] BRADLEY C. Automated surface roughness measurement[1]. The InternationalJournal of Advanced Manufacturing Technology ,2000,16(9) :668-674.[4] Li Chenggui, Li xing-shan, Qiang XI-FU 3D surface topography measurement method[J]. Aerospace measurement technology, 2000, 20(4): 2-10.[5] Liu He. Digital image processing and application [ M]. China Electric Power Press,2005译文：数字图像处理在铸件表面粗糙度测量中的应用摘要—本文提出了一种表面图像采集基于数字图像处理技术的系统。
外文翻译---特征空间稳健性分析：彩色图像分割
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 of thresholding 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). Thisinformation 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 embeddedinto 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 for sake of clarity only, later will be removed. A sphere X S of radius r, centered on x contains the featurevectors y such that r 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 criterioncolor 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**L space, whose coordinates are related to the RGB values by nonlinear uD was used as reference illuminant. The transformations. The daylight standard65chromatic information is carried by *u and *v, while the lightness coordinate *L can be regarded as the relative brightness. Psychophysical experiments show that *v**L space may not be perfectly isotropic, however, it was found satisfactory for uimage understanding applications. The image capture/display operations alsointroduce 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 seem s 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 outdoor scenes.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 locationcandidates 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 nei ghbors 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 selected searchN.window no longer exceedsmin6. [ID] Determining the initial feature palette.N vectors.In the feature space a significant color must be based on minimumminN pixels Similarly, to declare a color significant in the image domain more thanminof that color should belong to a connected component. From the extracted colors only those are retained for the initial feature palette which yield at least one connectedN. The neighbors removed at Step 4 component in the image of size larger thanminare also considered when defining the connected components Note that the threshold N which is used only at the post processing stage.is notcon7. [ID+FS] Determining the final feature palette.The initial feature palette provides the colors allowed when segmenting the image. If the palette is not rich enough the segmentation resolution was not chosen correctly and should be increased to the next class. All the pixel are reallocated basedon this palette. First, the pixels yielding feature vectors inside the search windows at their final location are considered. These pixels are allocated to the color of the window center without taking into account image domain information. The windowsare 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 thanN.Thesecon pixels are allocated to the majority color in their 3⨯3 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 notshown 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 level images, 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: 21/21(a)Original image, 512colors.（a）（b）Figure 4: Performance comparison.116⨯pixels, 200 colors. (b) Undersegmentation: 5/4 colors.(a) Original image, 261Region boundaries.（a）（b）（c）（d）（e）（f）Figure 5: The three segmentation classes for the house image. The right columnshows the region boundaries.(a)(b) Undersegmentation. Number of colors extracted initially and in the featurepalette: 8/8.(c)(d) Oversegmentation: 24/19 colors. (e)(f) Quantization: 49/37 colors.（a）（b）（c）256 Figure 6: Gray level image segmentation example. (a)Original image, 256pixels.(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。
图像的分割和配准中英文翻译
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。
otsu算法——图像分割
背景比例：
像素点总数：
前景和背景概率之和：
平均灰度值：
类间方差：
将公式（4）和（5）带入（6）可以得到等价公式：
核心代码：Histogram[data[i*srcimage.step + j]]++;//step指向每行的字节总量，date访问每个像素的值for (int i = 1; i < 255 ;i++)//从1开始遍历，寻找最合适的值{//每次遍历前需要初始化各变量w0 = 0; u0 = 0; w1 = 0; u1 = 0;for (int j = 0; j <= i; j++)//背景部分各值计算 { w0 += Histogram[j]; //背景部分像素点总数 u0 += j*Histogram[j]; //背景部分像素总灰度和 } u0 = u0 / w0; //背景像素平均灰度 w0 = w0 / number; //背景部分像素点所占比例}double varValueI = w1*w2*(u1 - u2)*(u1 - u2); //类间方差计算
算法过程：（1）设K(x,y)=f(x,y)/g(x,y)为像素点的 斜率，其中f(x,y)为点(x,y)的灰度值， g(x,y)为点(x,y)周围点的平均值。 （2）设阈值t1，t2将二维直方图分为A、B、 C三个区域。其中B区域代表前景和背 景像素点部分，而A、C代表边界点和 噪声点部分。
算法过程：（1）对于图像I(x,y),将前景与背景的分割阈值设为T。（2）将属于前景的像素点的个数占整个图像的比例设为w0，其平均灰度设为u0。（3）将属于背景的像素点的个数占整个图像的比例设为w1，其平均灰度设为u1。（4）图像的总平均灰度设为u，类间方差设为S。 假设图片的大小为M*N，图像中像素灰度值小于阈值T的像素个数记为N0，像素灰度大于阈值T的像素个数记为N1。则它们之间的关系如下。
基于UNet结构改进的医学影像分割技术综述
2、UNet模型改进的技术原理和 实现方法
2.1增加网络深度
增加网络深度是提高UNet模型性能的一种有效方法。通过增加编码器和解码 器的层数，可以使得模型具有更强的特征提取能力和更细致的空间信息捕捉能力。 然而，增加网络深度也会带来计算量和参数量增加的问题。一些研究通过使用轻 量级的网络结构（如MobileNetV2、ShuffleNetV2等）来平衡网络深度和计算效 率。
然而，UNet结构仍存在一些问题和不足，需要进一步改进和完善。因此，本 次演示将综述基于UNet结构改进的医学影像分割技术，以期为相关研究提供参考 和借鉴。
文献综述
UNet结构是一种全卷积网络架构，最早由Jens Petersen等人提出，并广泛 应用于医学影像分割任务。UNet结构由编码器和解码器两部分组成，通过跳跃连 接的方式将编码器中的低级特征图传递给解码器中的相应位置，从而保留更多的 空间信息。然而，传统的UNet结构仍存在一些问题和不足，如上下采样过程中的 信息丢失、梯度消失等问题。针对这些问题，研究者们提出了许多改进方法。
总之，U-Net是医学图像分割领域的一种强大工具，其表现出的特性和性能 在许多医学图像处理任务中取得了显著的成功。尽管仍存在一些挑战，如鲁棒性 和资源限制问题，但随着新技术的不断发展，我们有理由相信这些问题会被逐步 解决。未来对于U-Net及其变体的进一步研究，将为医学图像分割提供更准确、 更有效的方法。
U-Net在医学图像分割任务中表现出极佳的性能。例如，在分割CT图像中的 肺组织、MRI图像中的脑组织、X光图像中的骨骼等任务中，U-Net都能够取得超 越传统图像处理算法的性能。同时，通过引入不同的改进策略，如残差连接、注 意力机制、多尺度特征融合等，可以进一步增强U-Net的性能。
A Threshold Selection Method from Gray-Level Histograms图像分割经典论文翻译(部分)
A Threshold Selection Method from Gray-Level Histograms[1][1]Otsu N, A threshold selection method from gray-level histogram. IEEE Transactions on System，Man，and Cybemetics，SMC-8，1978：62-66．一种由灰度直方图选取阈值的方法摘要介绍了一种对于画面分割自动阈值选择的非参数和无监督的方法。
最佳阈值由判别标准选择，即最大化通过灰度级所得到的类的方差。
这个过程很简单，是利用了灰度直方图0阶和第1阶的累积。
这是简单的方法扩展到多阈值的问题。
几种实验结果呈现也支持了方法的有效性。
一.简介选择灰度充分的阈值，从图片的背景中提取对象对于图像处理非常重要。
在这方面已经提出了多种技术。
在理想的情况下，直方图具有分别表示对象和背景的能力，两个峰之间有很深的明显的谷，使得阈值可以选择这个谷底。
然而，对于大多数实际图片，它常常难以精确地检测谷底，特别是在这种情况下，当谷是平的和广泛的，具有噪声充满时，或者当两个峰是在高度极其不等，通常不产生可追踪的谷。
已经出现了，为了克服这些困难，提出的一些技术。
它们是，例如，谷锐化技术[2]，这个技术限制了直方图与（拉普拉斯或梯度）的衍生物大于绝对值的像素，并且描述了绘制差分直方图方法[3]，选择灰度级的阈值与差的最大值。
这些利用在原始图象有关的信息的相邻像素（或边缘），修改直方图以便使其成为阈值是有用的。
另一类方法与参数方法的灰度直方图直接相关。
例如，该直方图在最小二乘意义上与高斯分布的总和近似，应用了统计决策程序 [4]。
然而，这种方法需要相当繁琐，有时不稳定的计算。
此外，在许多情况下，高斯分布与真实模型的近似值较小。
在任何情况下，没有一个阈值的评估标准能够对大多数的迄今所提出的方法进行评价。
这意味着，它可能是派生的最佳阈值方法来建立一个适当的标准，从更全面的角度评估阈值的“好与坏”的正确方法。
unet 分割毕业设计
unet 分割毕业设计UNET（U-Net）是一种常用于图像分割的神经网络架构，它由Ronneberger等人在2015年提出，并被广泛应用于医学图像分割等领域。
UNET的设计灵感来源于编码器-解码器结构，具有“U”字形的网络拓扑结构。
UNET网络由两部分组成：下采样路径（编码器）和上采样路径（解码器）。
下采样路径通过卷积和池化操作逐渐减小特征图的尺寸，同时增加特征图的通道数，以提取图像的高级特征。
上采样路径通过反卷积和特征图连接的方式逐渐恢复原始图像的尺寸，同时减少特征图的通道数，以实现像素级的分类和分割。
UNET网络的特点是在上采样路径中使用了跳跃连接（skip connection），即将下采样路径中的特征图与上采样路径中的对应特征图进行连接，从而保留了更加细节的信息，提高了分割的准确性。
此外，UNET还引入了辅助分类器（auxiliary classifier）用于提供额外的监督信号，进一步增强了分割性能。
在进行分割任务时，UNET网络通常通过交叉熵损失函数（cross-entropy loss）来评估分割结果的准确性，并通过反向传播（backpropagation）和优化算法（如梯度下降）来更新网络参数，以最小化损失函数。
对于UNET的毕业设计，可以选择一个特定的图像分割任务，如医学图像分割、自然场景图像分割等，并使用UNET网络进行模型训练和评估。
具体的设计可以包括数据集的获取和预处理、UNET网络的搭建和训练、评估指标的选择和分析等方面。
同时，可以考虑使用一些增强技术，如数据增强、迁移学习等来提升分割性能。
总结来说，使用UNET进行分割毕业设计需要从具体的分割任务出发，设计实验并进行数据处理、网络搭建、训练和评估，在实践中不断优化和改进，最终得到准确且可靠的分割结果。
图像分割技术研究--毕业论文
本科毕业论文图像分割技术研究Survey on the image segmentation学院名称：电气信息工程学院专业班级：电子信息工程0601班2010年 6 月图像分割技术研究摘要图像分割是图像分析的第一步，是计算机视觉的基础，是图像理解的重要组成部分，也是图像处理、模式识别等多个领域中一个十分重要且又十分困难的问题。
在图像处理过程中，原有的图像分割方法都不可避免的会产生误差，这些误差会影响到图像处理和识别的效果。
遗传算法作为一种求解问题的高效并行的全局搜索方法，以其固有的鲁棒性、并行性和自适应性，使之非常适于大规模搜索空间的寻优，已广泛应用许多学科及工程领域。
在计算机视觉领域中的应用也正日益受到重视，为图像分割问题提供了新而有效的方法。
本文对遗传算法的基本概念和研究进展进行了综述；重点阐述了基于遗传算法的最大类间方差进行图像分割算法的原理、过程，并在MATLAB中进行了仿真实现。
实验结果表明基于遗传算法的最大类间方差方法的分割速度快，轮廓区域分割明显，分割质量高，达到了预期目的。
关键字：图像分割；遗传算法；阈值分割Survey on the image segmentationAbstract I mage segmentation is the first step of image processing and the basic of computer vision. It is an important part of the image, which is a very important and difficult problem in the field of image processing, pattern recognition.In image processing process, the original method of image segmentation can produce inevitable errors and these errors can affect the effect of image processing and identification .This paper discusses the current situation of the genetic algorithms used in the image segmentation and gives some kind of principles and the processes on genetic algorithm of image segmentationIn this paper.It also descripts the basic concepts and research on genetic algorithms .It emphasizes the algorithm based on genetic and ostu and realizes the simulation on Matlab. The experimental results show that this method works well in segmentation speed，the outline of the division and separate areas of high quality and achieve the desired effect.Genetic algorithm (GA) is a sort of efficient，paralled，full search method with its inherent virtues of robustness，parallel and self-adaptive characters. It is suitable for searching the optimization result in the large search space. Now it has been applied widely and perfectly in many study fields and engineering areas. In computer vision field GA is increasingly attached more importance. It provides the image segmentation a new and effective method.Key words image segmentation；genetic algorithm；image threshold segmentation目录第一章绪论 (1)1.1本课题研究的背景、目的与意义 (1)1.2本课题研究的现状与前景 (2)1.3本论文的主要工作及内容安排 (3)第二章图像分割基本理论 (4)2.1图像分割基本概念 (4)2.2图像分割的体系结构 (4)2.3图像分割方法分类 (5)2.3.1阈值分割方法 (5)2.3.2边缘检测方法 (8)2.3.3区域提取方法 (9)2.3.4结合特定理论工具的分割方法 (10)2.4图像分割的质量评价 (11)第三章遗传算法相关理论 (12)3.1遗传算法的应用研究概况 (12)3.2遗传算法的发展 (12)3.3遗传算法的基本概念 (13)3.4遗传算法基本流程 (14)3.5遗传算法的构成 (14)3.5.1编码 (14)3.5.2确定初始群体 (14)3.5.3适应度函数 (15)3.5.4遗传操作 (15)3.5.5控制参数 (17)3.6遗传算法的特点 (18)第四章 MATLAB相关知识 (20)4.1MATLAB简介 (20)4.2MATLAB的主要功能 (20)4.3MATLAB的技术特点 (21)4.4遗传算工法具箱(S HEFFIELD工具箱) (22)第五章基于遗传算法的最大类间方差图像分割算法 (24)5.1最大类间方差法简介 (24)5.2基于遗传算法的最大类间方差图像分割 (25)5.3流程图 (26)5.4实验结果 (27)第六章总结与展望 (29)6.1全文工作总结 (29)6.2展望 (29)致谢 (30)参考文献 (31)附录 (32)第一章绪论1.1本课题研究的背景、目的与意义数字图像处理技术是一个跨学科的领域。
matlab图像处理外文翻译外文文献
matlab图像处理外文翻译外文文献附录A 英文原文Scene recognition for mine rescue robotlocalization based on visionCUI Yi-an(崔益安), CAI Zi-xing(蔡自兴), WANG Lu(王璐)Abstract：A new scene recognition system was presented based on fuzzy logic and hidden Markov model(HMM) that can be applied in mine rescue robot localization during emergencies. The system uses monocular camera to acquire omni-directional images of the mine environment where the robot locates. By adopting center-surround difference method, the salient local image regions are extracted from the images as natural landmarks. These landmarks are organized by using HMM to represent the scene where the robot is, and fuzzy logic strategy is used to match the scene and landmark. By this way, the localization problem, which is the scene recognition problem in the system, can be converted into the evaluation problem of HMM. The contributions of these skills make the system have the ability to deal with changes in scale, 2D rotation and viewpoint. The results of experiments also prove that the system has higher ratio of recognition and localization in both static and dynamic mine environments.Key words: robot location; scene recognition; salient image; matching strategy; fuzzy logic; hidden Markov model1 IntroductionSearch and rescue in disaster area in the domain of robot is a burgeoning and challenging subject[1]. Mine rescue robot was developed to enter mines during emergencies to locate possible escape routes for those trapped inside and determine whether it is safe for human to enter or not. Localization is a fundamental problem in this field. Localization methods based on camera can be mainly classified into geometric, topological or hybrid ones[2]. With its feasibility and effectiveness, scene recognition becomes one of the important technologies of topological localization.Currently most scene recognition methods are based on global image features and have two distinct stages: training offline and matching online.。
外文翻译----数字图像处理方法的研究(中英文)(1)
The research of digital image processing technique1IntroductionInterest in digital image processing methods stems from two principal application areas:improvement of pictorial information for human interpretation;and processing of image data for storage,transmission,and representation for autonomous machine perception.1.1What Is Digital Image Processing?An image may be defined as a two-dimensional function,f(x,y),where x and y are spatial(plane)coordinates,and the amplitude of f at any pair of coordinates(x,y)is called the intensity or gray level of the image at that point.When x,y,and digital image.The field of digital image processing refers to processing digital images by means of a digital computer.Note that a digital image is composed of a finite number of elements,each of which has a particular location and value.These elements are referred to as picture elements,image elements,pels,and pixels.Pixel is the term most widely used to denote the elements of a digital image.We consider these definitions in more formal terms in Chapter2.Vision is the most advanced of our senses,so it is not surprising that images play the single most important role in human perception.However,unlike human who are limited to the visual band of the electromagnetic(EM)spectrum,imaging machines cover almost the entire EM spectrum,ranging from gamma to radio waves.They can operate on images generated by sources that human are not accustomed to associating with image.These include ultrasound,electron microscopy,and computer-generated images.Thus,digital image processing encompasses a wide and varied field of application.There is no general agreement among authors regarding where image processing stops and other related areas,such as image analysis and computer vision,start. Sometimes a distinction is made by defining image processing as a discipline in which both the input and output of a process are images.We believe this to be a limiting and somewhat artificial boundary.For example,under this definition,even the trivial task of computing the average intensity of an image(which yields a single number)would not be considered an image processing operation.On the other hand, there are fields such as computer vision whose ultimate goal is to use computer to emulate human vision,including learning and being able to make inferences and take actions based on visual inputs.This area itself is a branch of artificial intelligence(AI) whose objective is to emulate human intelligence.This field of AI is in its earliest stages of infancy in terms of development,with progress having been much slower than originally anticipated.The area of image analysis(also called image understanding)is in between image processing and computer vision.There are no clear-cut boundaries in the continuum from image processing at one end to computer vision at the other.However,one useful paradigm is to consider three types of computerized processes is this continuum:low-,mid-,and high-ever processes.Low-level processes involve primitive operation such as image preprocessing to reduce noise,contrast enhancement,and image sharpening.A low-level process is characterized by the fact that both its input and output are images. Mid-level processing on images involves tasks such as segmentation(partitioning an image into regions or objects),description of those objects to reduce them to a form suitable for computer processing,and classification(recognition)of individual object. Amid-level process is characterized by the fact that its inputs generally are images, but its output is attributes extracted from those images(e.g.,edges contours,and the identity of individual object).Finally,higher-level processing involves“making sense”of an ensemble of recognized objects,as in image analysis,and,at the far end of the continuum,performing the cognitive function normally associated with vision. Based on the preceding comments,we see that a logical place of overlap between image processing and image analysis is the area of recognition of individual regions or objects in an image.Thus,what we call in this book digital image processing encompasses processes whose inputs and outputs are images and,in addition, encompasses processes that extract attributes from images,up to and including the recognition of individual objects.As a simple illustration to clarify these concepts, consider the area of automated analysis of text.The processes of acquiring an image of the area containing the text.Preprocessing that images,extracting(segmenting)the individual characters,describing the characters in a form suitable for computer processing,and recognizing those individual characters are in the scope of what we call digital image processing in this book.Making sense of the content of the page may be viewed as being in the domain of image analysis and even computer vision, depending on the level of complexity implied by the statement“making cense.”As will become evident shortly,digital image processing,as we have defined it,is used successfully in a broad rang of areas of exceptional social and economic value.The concepts developed in the following chapters are the foundation for the methods used in those application areas.1.2The Origins of Digital Image ProcessingOne of the first applications of digital images was in the newspaper industry,when pictures were first sent by submarine cable between London and NewYork. Introduction of the Bartlane cable picture transmission system in the early1920s reduced the time required to transport a picture across the Atlantic from more than a week to less than three hours.Specialized printing equipment coded pictures for cable transmission and then reconstructed them at the receiving end.Figure 1.1was transmitted in this way and reproduced on a telegraph printer fitted with typefaces simulating a halftone pattern.Some of the initial problems in improving the visual quality of these early digital pictures were related to the selection of printing procedures and the distribution ofintensity levels.The printing method used to obtain Fig.1.1was abandoned toward the end of1921in favor of a technique based on photographic reproduction made from tapes perforated at the telegraph receiving terminal.Figure1.2shows an images obtained using this method.The improvements over Fig.1.1are evident,both in tonal quality and in resolution.FIGURE1.1A digital picture produced in FIGURE1.2A digital picture 1921from a coded tape by a telegraph printer made in1922from a tape punched With special type faces(McFarlane)after the signals had crossed theAtlantic twice.Some errors areVisible.(McFarlane)The early Bartlane systems were capable of coding images in five distinct level of gray.This capability was increased to15levels in1929.Figure1.3is typical of the images that could be obtained using the15-tone equipment.During this period, introduction of a system for developing a film plate via light beams that were modulated by the coded picture tape improved the reproduction process considerably. Although the examples just cited involve digital images,they are not considered digital image processing results in the context of our definition because computer were not involved in their creation.Thus,the history of digital processing is intimately tied to the development of the digital computer.In fact digital images require so much storage and computational power that progress in the field of digital image processing has been dependent on the development of digital computers of supporting technologies that include data storage,display,and transmission.The idea of a computer goes back to the invention of the abacus in Asia Minor, more than5000years ago.More recently,there were developments in the past two centuries that are the foundation of what we call computer today.However,the basis for what we call a modern digital computer dates back to only the1940s with the introduction by John von Neumann of two key concepts:(1)a memory to hold a stored program and data,and(2)conditional branching.There two ideas are the foundation of a central processing unit(CPU),which is at the heart of computer today. Starting with von Neumann,there were a series of advances that led to computers powerful enough to be used for digital image processing.Briefly,these advances maybe summarized as follow:(1)the invention of the transistor by Bell Laboratories in1948;(2)the development in the1950s and1960s of the high-level programminglanguages COBOL(Common Business-Oriented Language)and FORTRAN (Formula Translator);(3)the invention of the integrated circuit(IC)at Texas Instruments in1958;(4)the development of operating system in the early1960s;(5)the development of the microprocessor(a single chip consisting of the centralprocessing unit,memory,and input and output controls)by Inter in the early 1970s;(6)introduction by IBM of the personal computer in1981;(7)progressive miniaturization of components,starting with large scale integration(LI)in the late1970s,then very large scale integration(VLSI)in the1980s,to the present use of ultra large scale integration(ULSI).Figure1.3In1929from London to Cenerale Pershingthat New York delivers with15level tone equipmentsthrough cable with Foch do not the photograph by decorationConcurrent with these advances were development in the areas of mass storage and display systems,both of which are fundamental requirements for digital image processing.The first computers powerful enough to carry out meaningful image processing tasks appeared in the early1960s.The birth of what we call digital image processing today can be traced to the availability of those machines and the onset of the apace program during that period.It took the combination of those two developments to bring into focus the potential of digital image processing concepts.Work on using computer techniques for improving images from a space probe began at the Jet Propulsion Laboratory(Pasadena,California)in1964when pictures of the moontransmitted by Ranger7were processed by a computer to correct various types of image distortion inherent in the on-board television camera.Figure1.4shows the first image of the moon taken by Ranger7on July31,1964at9:09A.M.Eastern Daylight Time(EDT),about17minutes before impacting the lunar surface(the markers,called reseau mark,are used for geometric corrections,as discussed in Chapter5).This also is the first image of the moon taken by a U.S.spacecraft.The imaging lessons learned with ranger7served as the basis for improved methods used to enhance and restore images from the Surveyor missions to the moon,the Mariner series of flyby mission to Mars,the Apollo manned flights to the moon,and others.In parallel with space application,digital image processing techniques began in the late1960s and early1970s to be used in medical imaging,remote Earth resources observations,and astronomy.The invention in the early1970s of computerized axial tomography(CAT),also called computerized tomography(CT)for short,is one of the most important events in the application of image processing in medical diagnosis. Computerized axial tomography is a process in which a ring of detectors encircles an object(or patient)and an X-ray source,concentric with the detector ring,rotates about the object.The X-rays pass through the object and are collected at the opposite end by the corresponding detectors in the ring.As the source rotates,this procedure is repeated.Tomography consists of algorithms that use the sensed data to construct an image that represents a“slice”through the object.Motion of the object in a direction perpendicular to the ring of detectors produces a set of such slices,which constitute a three-dimensional(3-D)rendition of the inside of the object.Tomography was invented independently by Sir Godfrey N.Hounsfield and Professor Allan M. Cormack,who shared the X-rays were discovered in1895by Wilhelm Conrad Roentgen,for which he received the1901Nobel Prize for Physics.These two inventions,nearly100years apart,led to some of the most active application areas of image processing today.Figure1.4The first picture of the moon by a U.S.Spacecraft.Ranger7took this image on July31,1964at9:09A.M.EDT,about17minutes beforeImpacting the lunar surface.(Courtesy of NASA.)中文翻译数字图像处理方法的研究1绪论数字图像处理方法的研究源于两个主要应用领域:其一是为了便于人们分析而对图像信息进行改进；其二是为了使机器自动理解而对图像数据进行存储、传输及显示。
数字图像处理论文中英文对照资料外文翻译文献
第 1 页中英文对照资料外文翻译文献原 文To image edge examination algorithm researchAbstract ：Digital image processing took a relative quite young discipline,is following the computer technology rapid development, day by day obtains th widespread application.The edge took the image one kind of basic characteristic,in the pattern recognition, the image division, the image intensification as well as the image compression and so on in the domain has a more widesp application.Image edge detection method many and varied, in which based on brightness algorithm, is studies the time to be most long, the theory develo the maturest method, it mainly is through some difference operator, calculates its gradient based on image brightness the change, thus examines the edge, mainlyhas Robert, Laplacian, Sobel, Canny, operators and so on LOG 。
normalized cuts and image segmentation翻译
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。
Figure1:H<iw m.3Uiyps?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一将一个图像的区域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。
transformer分割毕业设计
一、概述Transformer 分割（Transformer-based semantic segmentation）是一种基于 Transformer 模型进行图像像素级别语义分割的方法。
目前，传统的图像分割方法在处理复杂场景下效果并不理想，而基于Transformer 的分割方法在语义分割任务中取得了很大的进展，受到了广泛的关注。
本文将探讨基于 Transformer 的分割方法对毕业设计的意义和必要性。
二、Transformer 的原理及优势1. Transformer 模型的原理Transformer 是一种基于自注意力机制的深度学习模型，最初应用于自然语言处理领域。
其特点是通过注意力机制实现了序列数据的全局依赖性建模，因此在各种序列建模任务中取得了很好的效果。
2. Transformer 在图像领域的应用近年来，研究者们开始探索将 Transformer 模型应用于计算机视觉领域。
其中，基于 Transformer 的图像分割方法取得了一系列突破性进展。
相比传统的卷积神经网络（CNN）方法，基于 Transformer 的分割方法可以更好地处理长距离的依赖关系，改善了图像分割任务中的像素级别语义理解能力。
3. 基于 Transformer 的分割方法的优势a. 长距离依赖性建模能力更强：Transformer 模型可以轻松捕捉长距离像素之间的关联，有助于提高分割结果的准确性。
b. 参数效率高：相比传统的卷积神经网络方法，Transformer 模型在处理图像分割任务时，参数数量更少，计算效率更高。
c. 开放式调整便利：Transformer 模型的结构更具灵活性，可以根据任务需求进行不断调整和改进。
三、基于 Transformer 的分割方法在毕业设计中的意义1. 提高图像分割结果的准确性由于 Transformer 模型在捕捉长距离的依赖关系方面具有优势，因此基于Transformer 的分割方法能够在保证像素级别语义理解的基础上，进一步提高分割结果的准确性。
基于标准割(Normalized cut)算法图像分割方法
字表示图像的灰度值，通过权值公式计算两个像素的权值并寻 找出一条权值和最小的路径完成图像分割。
图 ３ 图像分割
图 ３在使用 ＭＡＴＬＡＢ软件进行的图论分割方法分割出来 不同的等级，然后设定一个阈值与图像中不同的灰度等级进行
的图像，在 ＭＡＴＬＡＢ中通过计算灰度级强度差找到权值求和最 比较，根据比较将图像灰度像素分成两个区域，分别是目标区
１ 图像分割简介
图像分割是图像处理中的一项至关重要的技术。自 ２０世 纪 ７０年代开始，图像分割技术一直备受人们的高度重视，到目 前为止已经有很 多 的 分 割 方 法，从 图 像 分 割 的 依 据 出 发，分 割 方法分为非连 续 性 分 割 和 相 似 性 分 割 ［１］。近 年 来 有 很 多 不 同 领域的学者们提出的新理论、新方法与图像分割相结合而形成 多种新型分割技术，如 聚 类 分 析、图 理 论、小 波 变 换、人 工 神 经 网络等分割方法，这 些 多 领 域、多 类 型 的 分 割 方 法 的 提 出 对 于 图像分割技术的完善有着重要的作用。图 １是一般的图像处 理过程，从图中可以看出图像分割是介于图像预处理和图像识 别之间，在图像处 理 当 中 起 着 承 上 启 下 的 作 用，一 方 面 它 是 准 确提取目标物的处理手段，使人们能够得到有意义的感兴趣对 象，另一方面它能够做到目标准确的识别，特征准确的提取，参 数准确的测量等使图像变得更为简明、抽象的形式展现在观察 者面前，更容易进 行 视 觉 分 析 和 模 式 识 别，并 且 对 后 续 的 图 像 识别、分析和理解能够达到跟高的层次。
图 １ 图像处理过程
图 ２ 图论分割原理
收稿日期：２０１８－０５－１６ 作者简介：安文波（１９９２—），男，辽宁沈阳人，硕士研究生。
毕业论文要求毕业论文(设计说明书)撰写规范
毕业论文（设计说明书）撰写规范毕业设计（论文）是学生在校学习成果的集中体现，毕业论文或毕业设计说明书是学生提交毕业设计（论文）资料中的主要部分。
为了提高我校的毕业设计（论文）质量，使毕业论文（设计说明书）在内容和格式上更加统一规范，特编写此规范。
一、论文内容要求1．毕业论文字数根据专业及课题不同要求在8000字以上，论文内容应完整、准确，层次分明，数据可靠，文字简练，分析透彻，推理严谨，立论正确。
毕业设计说明书字数不低于8000字。
2．论文撰写前应翻译完整的外文文献1～2篇（中文字数不低于3000字），要求翻译的内容与课题相关；撰写与课题内容相关的文献综述2000字以上。
3．论文应采用国家正式公布实施的简化汉字、法定计量单位和国家制图标准。
4．论文采用的术语、符号、代号全文必须统一，并符合规范要求。
论文中使用新的专业术语、缩略语、习惯用语，应加以注释。
5．文稿中的插图、照片必须确保能复制或微缩。
二、论文各部分要求论文内容一般应由十个主要部分组成，依次为：(1)封面，(2)中文摘要，(3)英文摘要，(4)关键字，(5)目录，(6)前言，(7)论文正文，(8)参考文献，(9)附录，(10)致谢。
各部分的具体要求如下：1．封面采用学校统一的封面格式，封面上填写论文题目、作者姓名、学号、所在院（系）、专业名称、指导教师姓名及完成日期。
论文题目不宜过长，一般不超过25个字。
2．中文摘要摘要是论文不加注释和评论的简短陈述，具有独立性和自含性，摘要中有数据、有结论，是一篇完整的短文，可以独立使用和引用，论文摘要在写法上一般不分段落，常采用无人称句。
摘要中一般不用图表、化学反应式、数学表达式等，不能出现非通用性的外文缩略语或代号，不得引用参考文献。
写作论文摘要时应注意能反映出以下几方面的内容：论文所研究的问题及其目的和意义；论文的基本思路和逻辑结构；问题研究的主要方法、内容、结果和结论。
论文摘要一般200～400字。
图像分割研究综述
编者按:在人类接收的信息中有80%来自视觉或者说为图像(Image)信息,这包括图像、图形(动画)、视频、文本、数据等,这是人类最有效和最重要的信息获取和交流方式。
随着计算机的普及,人们越来越多地利用计算机来帮助人类获取与处理视觉(图像)信息。
图像技术是对视觉图像获取与加工处理技术的总称。
图像技术近年来受到人们广泛的关注,它包括图像的采集获取;图像编码存储和传输;图像的产生、显示与变换;图像的分割,图像的特征提取与测量;图像数据库的建立、索引和抽取;图像的分类、表示和识别;图像的模型匹配;图像的质量评价;图像的解释与理解等。
根据抽象程度和处理方法的不同,图像技术可分为三个层次:图像处理,图像分析和图像理解。
这三个层次的有机结合也可称为图像工程。
图像处理是较低层的操作,主要在图像像素级上进行处理。
比较狭义的图像处理主要包括对图像分割以改善视觉效果,或对图像压缩编码以减少传输时间或存储容量。
图像分析则是进入中层的操作,分割和特征提取把原来以像素描述的图像转变成简洁的非图形式的符号描述。
即图像分析是一个图像进而数据出的处理,数据可以是特征测量的结果,或是基于测量的符号表示。
图像理解也经常被称为计算机视觉,这主要是高层操作。
图像理解进一步研究图像中的目标和它们之间的联系,其处理过程与方法与人类的思维推理往往有不少类似之处。
根据以上所述,本专辑有以下文章:①图像分割研究综述;②视觉计算———人类感知能力的延伸;③数字图像的质量评价;④基于数码相机的图像采集系统;⑤神经递质释放的计算机图像分析系统;⑥计算机视觉技术在智能交能系统中的应用;⑦点光源视景图像的控制与实现图像分割研究综述3Study Surveys on Image Segmentation北京工业大学信号与信息处理研究室(100022) 王爱民 沈兰荪【摘要】图像分割是指将一幅图像分解为若干互不交迭区域的集合,是图像处理与机器视觉的基本问题之一。
图像分割毕业论文
第一章绪论1.1课题的研究背景及意义随着信息技术的开展和不断深入，人们越来越多的利用计算机来帮助人类获取与处理各种信息。
据统计，在人类从外界获得的信息中有75%左右是来自视觉或者说图像信息，它是人类最有效的信息获取和交流方式，图像也因为其所含的信息量大、表现直观而在近年得到的广为宣传和应用的多媒体中占据了重要的地位。
图像分割是一种根本的计算机视觉技术，是从图像处理到图像分析的关键步骤。
图像分割就是将图像表示为物理上有意义的连通区域的集合，是进行图像分析的第一步工作，也是解决起来比较困难的一个问题。
对于那些基于图像分割结果的接下来的任务，如特征提取、目标识别等的质量的好坏都取决于是否有一个质量比较好的图像分割结果，有效合理的图像分割能够为基于内容的图像检索、对象分析等抽象出十分有用的信息，从而使得更高层的图像理解成为可能[1]。
1.1.1图像分割在数字图像处理中的地位为了弄清图像分割在数字图像处理中所处的地位，我们引入并使用“图像工程〞这个概念。
图像技术在广义上是各种与图像有关技术的总称。
图像技术种类很多，跨度很大，但可以将它们归在一个整体框架一一“图像工程〞之下。
图像工程是一个对整个图像领域进行研究应用的新科学，它的内容非常丰富，根据抽象程度和研究方法等的不同可分为三个有特点的层次〔如图1-1所示〕：图像处理、图像分析和图像理解[2]。
图1-1图像分割在图像工程中的位置图像处理着重强调在图像之间进行变换以改善图像的视觉效果。
图像分析那么主要是对图像中感兴趣的目标进行监测和测量，以获得它们的客观信息从而建立对图像的描述。
图像理解的重点是在图像分析的根底上，进一步研究图像中各目标的性质和它们之间的相互联系，并得出对原始成像客观场景的解释，从而指导和规划行动。
图像处理、图像分析和图像理解具有不同的操作对象，参考图1-1图像处理是比较低层的操作，它主要在图像像素级上进行处理。
图像分析那么进入了中层，它侧重于对像素集合到目标的表达测量描述。
基于区域的分割与基于形态学分水岭的分割(部分)
本科毕业设计外文翻译外文译文题目：基于区域的分割与基于形态学分水岭的分割（部分）学院: 信息科学与工程学院专业: 自动化学号: 200604134167学生姓名: 成俊涛指导教师: 王斌日期: 2010年6月6日《Digital Image Processing》（Second Edition）10.4 Region-Based Segmentation10.5 Segmentation by Morphological Watersheds（Part）（From page 612 to page 622）By Rafael C. Gonzalez and Richard E. WoodsPublishing House of Electronics IndustryBeijingMarch, 2008《数字图像处理》（第二版）10.4基于区域的分割10.5基于形态学分水岭的分割（部分）（612页至622页）Rafael C. Gonzalez and Richard E. Woods著电子工业出版社北京2008年3月10.4 基于区域的分割分割的目的是将图像划分为不同区域。
在第10.1和10.2节中，我们根据基于区域间灰度层次不连续性质通过搜寻边界来解决这个问题，而10.3节是通过对像素的属性，如灰度值或颜色，阈值的分布进行分割来完成。
在本节中我们讨论的是直接寻找区域为基础的分割技术。
10.4.1 基本公式设R 代表整个图像区域。
我们可以认为这是一个将区域R 分割成n 个区域R1，R2 ,..., Rn 的过程：(a) n1i i R R ==； (b)i R 是一个连通区域, i = 1,2 ..... n ； (c)i j R R =∅对所有的i 、j, i ≠j ； (d)()i P R TRUE =， i = 1,2 ..... n ； (e) ()i j P R R FALSE =对任意相邻区间 Ri 和 Rj 。
图像分割文献综述
文献综述图像分割就是把图像分成各具特色的区域提取感兴趣目标的技术和过程。
它是由图像处理到图像分析的关键步骤，是一种基本的计算机视觉技术。
图像分割起源于电影行业。
伴随着近代科技的发展，图像分割在实际中得3到了广泛应用，如在工业自动化、在线产品检验、生产过程控制、文档图像处理、遥感和生物医学图像分析、以及军事、体育、农业工程等方面。
总之，只要是涉及对对象目标进行特征提取和测量，几乎都离不开图像分割。
所以，对图像分割的研究一直是图像工程中的重点和热点。
自图像分割的提出至今，已经提出了上千种各种类型的分割算法。
由于分割算法非常多，所以对它们的分类方法也不尽相同。
我们依据使用知识的特点与层次，将其分为基于数据和基于模型两大类。
前者是直接对当前图像的数据进行操作，虽然可以利用相关的先验信息，但是不依赖于知识；后者则是直接建立在先验知识的基础上，这类分割更符合当前图像分割的技术要点，也是当今图像分割的主流。
基于数据的图像分割算法多数为传统算法，常见的包括，基于边缘检测，基于区域以及边缘与区域相结合的分割方法等等。
这类分割方法具有以下缺点，○1易受噪声和伪边缘影响导致得到的边界不连续，需要用特定的方法进行连接；○2只能提取图像局部特征，缺乏有效约束机制，难以获得图像的全局信息；○3只利用图像的底层视觉特征，难以将图像的先验信息融合到高层的理解机制中。
这是因为传统的图像处理算法都是基于MIT人工智能实验室Marr提出的各层相互独立、严格由低到高的分层视觉框架下进行的。
由于各层之间不存在反馈，数据自底向上单向流动，高层的信息无法指导底层特征的提取，从而导致底层的误差不断积累，且无法修正。
基于模型的分割方法则可以克服以上缺陷。
基于模型的分割方法可以将分割目标的先验知识等有用信息融合到高层的理解机制之中，并通过对图像中的特定目标对象建模来完成分割任务。
这是一种自上而下的处理过程，可以将图像的底层视觉特征与高层信息有机结合起来，因此更接近人类的视觉处理。
图像分割技术在医学图像处理应用论文
图像分割技术在医学图像处理中的应用研究摘要：通过图像分割技术在医学图像处理中的应用研究，深入理解各种分割方法的理论基础、应用价值以及优缺点，着重研究基于变形模型的分割方法在医学图像分割中的应用，研究该方法的优缺点并提出相应的改进算法。
关键词：图像；分割方法中图分类号：tp399 文献标识码：a 文章编号：1007-9599 (2011) 22-0000-01picture partitions technology application study in the medical science picture processingyang jiaping(wuxi teachers’ college,wuxi 214000,china)abstract:pass a picture partition technique in the medical science picture application study within processing,go deep into to comprehend various theory foundation,applied value and merit and shortcoming that partition a method and emphasize research according to transform the partition method of model partitions in the medical science picture in of application,study the merit and shortcoming of the method and put forward homologous improvement calculate way.keywords:picture;partition a method随着多媒体技术的迅速发展，在现代医学中，医学成像技术已成为其重要分支和不可或缺的诊断、治疗及研究工具。
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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。
关于门限处理的讨论将在几种面向区域的分割方法展开的讨论之后进行。
之后，我们将讨论一种称为分水岭分割法的形态学图像分割方法。
这种方法特别具有吸引力，因为它将本章第一部分提到的几种分割属性技术结合起来了。
我们将以图像分割的应用方面进行讨论来结束本章。
10.1间断检测在本节中，我们介绍几种用于检测数字图像中三种基本的灰度级间断技术:点、线和边缘。
寻找间断最一般的方法是以3.5节中描述的方式对整幅图像使用一个模板进行检测。
图10-1所示的3x3模板，这一过程包括计算模板所包围区域内灰度级与模板系数的乘积之和。
就是说，关于式(3.5.3)，在图像中任意点的模板响应由下列公式给出：∑==+++=9199...2211i wiziz w z w z w R （10.1.1）图10-1 一个一般的3*3模板这里Zi 是与模板系数Wi 相联系的像素的灰度级。
照例，模板响应是它的中心位置。
有关执行模板操作的细节在3.5节中讨论。
10.1.1点检测在一幅图像中，孤立点的检测在理论上是简单的。
使用如图10-2(a)所示的模板，如果|R| ≥ T (10.1.2)我们说在模板中心的位置上已经检测到一个点。
这里T 是一个非负门限，R 由式(10.1.1)给出。
基本上，这个公式是测量中心点和它的相邻点之间加权的差值。
基本思想就是:如果一个孤立的点(此点的灰度级与其背景的差异相当大并且它所在的位置是一个均匀的或近似均匀的区域)与它周围的点很不相同，则很容易被这类模板检测到。
注意，图10-2(a)中的模板同图3.39(d)中给出的模板在拉普拉斯操作方而是相同的。
严格地讲，这里强调的是点的检测。
即我们着重考虑的差别是那些足以识别为孤立点的差异(由T决定)。
注意，模板系数之和为零表示在灰度级为常数的区域，模板响应为零。
-1 -1 -1-1 8 -1-1 -1 -1（a）（b）（c）（d）图10-2 （a）点检测模板，（b）带有通孔的涡轮叶片的X射线，（c）点检测的结果，（d）使用式（10.1.2）得到的结果（原图由X-TEK系统公司提供）例10.1图像中孤立点的检浏我们以图10-2(b)功为辅助说明如何从一幅图中将孤立点分割出来.这幅X射线图显示了一个带有通孔的喷气发动抓涡枪叶片，通孔位于圈像的右上象限。
在孔中只嵌有一个黑色像素。
图10-2(c)是将点检测模板应用于X射线图像后得到的结果.图10-2(d)显示了当T取图10-2(c)中像素最高绝衬值的90%时，应用式(10.1.2)所得的结果(门限选择将在10.3节中详细讨论)。
图中的这个单一的像素清晰可见(这个像素被人为放大以便印刷后可以看到)。
由于这类检测是基于单像素间断，并且检测器模板的区域有一个均匀的背景，所以这个检测过程是相当有专用性的当这一条件不能满足时，本章中计论的其他方法会更适合检测灰度级间断10.1.2线检测复杂程度更高一级的检测是线检测，考虑图10-3中显示的模板。
如果第l个模板在图像中移动，这个模板将对水平方向的线条(一个像素宽度)有更强的响应。
在一个不变的背景上，当线条经过模板的中间一行时会产生响应的最大值。
画一个元素为1的简单阵列，并且使具有不同灰度级(如5)的一行水平穿过阵列，可以很容易验证这一点。
同样的实验可以显示出图10-3中的第2个模板对于45°方向线有最佳响应;第3个模板对于垂直线有最佳响应;第4个模板对于-45°方向线有最佳响应;这些方向也可以通过注释每个模板的优选方向来设置，即在这些方向上用比别的方向更大的系数(为2)设置权值。
注意每个模板系数相加的总和为零，表示在灰度级恒定的区域来自模板的响应为零。
°图10-3 线模板令R1，R2，R3和R4。
从左到右代表图10-3中模板的响应，这里R的值由式(10.1.1)给出。
假设4个模板分别应用于一幅图像，在图像中心的点，如果|Ri|>|Rj| ,j≠i，则此点被认为与在模板i方向上的线更相关。
例如，如果在图中的一点有|Ri|>|Rj| ,j=2,3,4，我们说此特定点与水平线有更大的联系。
换句话说，我们可能对检测特定方向上的线感兴趣。
在这种情况下，我们应使用与这一方向有关的模板，并设置该模板的输出门限，如式(10.1.2)所示。
换句话说，如果我们对检测图像中由给定模板定义的方向上的所有线感兴趣.只需要简单地通过整幅图像运行模板，并对得到的结果的绝对值设置门限即可。
留下的点是有最强响应的点。
对于一个像素宽度的线，这些响应最靠近模板定义的对应方向。
下列例子说明了这一过程。
例 10.2特定方向上的线检测图10-4(a)显示了一幅电路接线模板的数字化(二值的)图像。
假设我们要找到一个像素宽度的并且方向为-45°的线条。
基于这个假设，使用图10-3中最后一个模板。
图10-4(b)显示了得到的结果的绝对值。
注意，图像中所有水平和垂直的部分都被除去了。
并且在图10-4(b)中所有原图中接近-45°方向的部分产生了最强响应。
（a）（b）（c）图10-4 线检测的说明。
（a）二进制电路接线模板，（b）使用-45°线检测器处理后得到的绝对值，（c）对图像（b）设置门限得到的结果为了决定哪一条线拟合模板最好，只需要简单地对图像设置门限。
图10-4(c)显示了使门限等于图像中最大值后得到的结果。
对于与这个例子类似的应用，让门限等于最大值是一个好的选择，因为输入图像是二值的，并且我们要寻找的是最强响应。
图10-4(c)显示了在白色区所有通过门限检测的点。
此时，这一过程只提取了一个像素宽且方向为-45°的线段(图像中在左上象限中也有此方向上的图像部分，但宽度不是一个像素)。
图10-4(c)中显示的孤立点是对于模板也有相同强度响应的点。
在原图中，这些点和与它们紧接着的相邻点，是用模板在这些孤立位置上生成最大响应的方法来定向的。
这些孤立点也可以使用图10-2(a)中的模板进行检测，然后删除，或者使用下一章中讨论的形态学腐蚀法删除。
10.1.3边缘检侧尽管在任何关于分割的讨论中，点和线检测都是很重要的，但是边缘检测对于灰度级间断的检测是最为普遍的检测方法。
本节中，我们讨论实现一阶和二阶数字导数检测一幅图像中边缘的方法。
在3.7节介绍图像增强的内容中介绍过这些导数。
本节的重点将放在边缘检测的特性上。
某些前面介绍的概念在这里为了叙述的连续性将进行简要的重述。
基本说明在3.7.1节中我们非正式地介绍过边缘。
本节中我们更进一步地了解数字化边缘的概念。
直观上，一条边缘是一组相连的像素集合。
这些像素位于两个区域的边界上。
然而，我们已经在2.5.2节中用一定的篇幅解释了一条边缘和一条边界的区别。
从根本上讲，如我们将要看到的，一条边缘是一个“局部”概念，而由于其定义的方式，一个区域的边界是一个更具有整体性的概念。
给边缘下一个更合理的定义需要具有以某种有意义的方式测量灰度级跃变的能力。
我们先从直观上对边缘建模开始。
这样做可以将我们引领至一个能测量灰度级有意义的跃变的形式体系中。
从感觉上说，一条理想的边缘具有如图10-5(a)所示模型的特性。
依据这个模型生成的完美边缘是一组相连的像素的集合(此处为在垂直方向上)，每个像素都处在灰度级跃变的一个垂直的台阶上(如图形中所示的水平剖面图)。
实际上，光学系统、取样和其他图像采集的不完善性使得到的边缘是模糊的，模糊的程度取决于诸如图像采集系统的性能、取样率和获得图像的照明条件等因素。
结果，边缘被更精确地模拟成具有“类斜面”的剖面，如图10-5(b)所示。
斜坡部分与边缘的模糊程度成比例。
在这个模型中，不再有细线(一个像素宽的线条)。
相反，现在边缘的点是包含于斜坡中的任意点，并且边缘成为一组彼此相连接的点集。
边缘的“宽度”取决于从初始灰度级跃变到最终灰度级的斜坡的长度。
这个长度又取决于斜度，斜度又取决于模糊程度。
这使我们明白:模糊的边缘使其变粗而清晰的边缘使其变得较细。
图10-6(a)显示的图像是从图10-5(b)的放大特写中提取出来的。
图10-6(b)显示了两个区域之间边缘的一条水平的灰度级剖面线。
这个图形也显示出灰度级剖面线的一阶和二阶导数。
当我们沿着剖面线从左到右经过时，在进人和离开斜面的变化点，一阶导数为正。
在灰度级不变的区域一阶导数为零。
在边缘与黑色一边相关的跃变点二阶导数为正，在边缘与亮色一边相关的跃变点二阶导数为负，沿着斜坡和灰度为常数的区域为零。
在图10-6(b)中导数的符号在从亮到暗的跃变边缘处取反。
（a）（b）图10-5 （a）理想的数字边缘模型，（b）斜坡数字边缘模型。
斜坡部分与边缘的模糊程度成正比图10-6 (a)由一条垂直边缘分开的两个不同区域,(b)边界附近的细节显示了一个灰度级剖面图和一阶与二阶导数的剖面图由这些现象我们可以得到的结论是:一阶导数可以用于检测图像中的一个点是否是边缘的点(也就是判断一个点是否在斜坡上)。