外文翻译---MATLAB 在图像边缘检测中的应用

canny边缘检测matlab代码

canny边缘检测matlab代码Canny边缘检测是一种常用的图像处理算法，它可以有效地检测图像中的边缘，并将其显示为白色线条。

在Matlab中，可以使用以下代码实现Canny边缘检测：1. 读取图像首先，需要读取待处理的图像。

可以使用imread函数来读取图片：```matlabimg = imread('image.jpg');```其中，image.jpg是待处理的图片文件名。

2. 灰度化Canny算法只能处理灰度图像，因此需要将彩色图像转换为灰度图像。

可以使用rgb2gray函数来实现：```matlabgray_img = rgb2gray(img);```3. 高斯滤波在进行边缘检测之前，需要对图像进行高斯滤波来消除噪声。

可以使用fspecial和imfilter函数来实现：```matlabgaussian_filter = fspecial('gaussian', [5 5], 1);blur_img = imfilter(gray_img, gaussian_filter, 'replicate');```其中，[5 5]表示高斯核的大小为5x5，1表示标准差。

4. 计算梯度幅值和方向接下来，需要计算每个像素点的梯度幅值和方向。

可以使用Sobel算子来计算梯度，并利用arctan函数计算方向角度：```matlabsobel_x = [-1 0 1; -2 0 2; -1 0 1];sobel_y = [-1 -2 -1; 0 0 0; 1 2 1];grad_x = imfilter(blur_img, sobel_x, 'replicate');grad_y = imfilter(blur_img, sobel_y, 'replicate');grad_mag = sqrt(grad_x.^2 + grad_y.^2);grad_dir = atan(grad_y ./ grad_x);```5. 非极大值抑制由于Sobel算子计算出的梯度幅值可能会有多个峰值，因此需要进行非极大值抑制来保留边缘。

基于Matlab的图像边缘检测算法的实现及应用汇总

目录摘要 (1)引言 (2)第一章绪论 (3)1.1 课程设计选题的背景及意义 (3)1.2 图像边缘检测的发展现状 (4)第二章边缘检测的基本原理 (5)2.1 基于一阶导数的边缘检测 (8)2.2 基于二阶导的边缘检测 (9)第三章边缘检测算子 (10)3.1 Canny算子 (10)3.2 Roberts梯度算子 (11)3.3 Prewitt算子 (12)3.4 Sobel算子 (13)3.5 Log算子 (14)第四章MATLAB简介 (15)4.1 基本功能 (15)4.2 应用领域 (16)第五章编程和调试 (17)5.1 edge函数 (17)5.2 边缘检测的编程实现 (17)第六章总结与体会 (20)参考文献 (21)摘要边缘是图像最基本的特征，包含图像中用于识别的有用信息，边缘检测是数字图像处理中基础而又重要的内容。

该课程设计具体考察了5种经典常用的边缘检测算子,并运用Matlab进行图像处理结果比较。

梯度算子简单有效，LOG 算法和Canny 边缘检测器能产生较细的边缘。

边缘检测的目的是标识数字图像中灰度变化明显的点，而导函数正好能反映图像灰度变化的显著程度，因而许多方法利用导数来检测边缘。

在分析其算法思想和流程的基础上，利用MATLAB对这5种算法进行了仿真实验，分析了各自的性能和算法特点，比较边缘检测效果并给出了各自的适用范围。

关键词：边缘检测；图像处理；MATLAB仿真引言边缘检测在图像处理系统中占有重要的作用，其效果直接影响着后续图像处理效果的好坏。

许多数字图像处理直接或间接地依靠边缘检测算法的性能，并且在模式识别、机器人视觉、图像分割、特征提取、图像压缩等方面都把边缘检测作为最基本的工具。

但实际图像中的边缘往往是各种类型的边缘以及它们模糊化后结果的组合，并且在实际图像中存在着不同程度的噪声，各种类型的图像边缘检测算法不断涌现。

早在1965 年就有人提出边缘检测算子，边缘检测的传统方法包括Kirsch，Prewitt，Sobel，Roberts，Robins，Mar-Hildreth 边缘检测方法以及Laplacian-Gaussian（LOG）算子方法和Canny 最优算子方法等。

Matlab中的图像分割与边缘检测方法

Matlab中的图像分割与边缘检测方法引言图像处理是一门研究如何对数字图像进行处理、分析、改进和理解的学科。

图像分割与边缘检测在图像处理中占据着重要的地位。

图像分割是将图像划分为多个具有语义意义的区域或对象的过程，而边缘检测则是找到图像中不连续的区域边界。

Matlab作为一种强大的软件工具，提供了丰富的图像处理函数和工具箱，本文将探讨在Matlab中应用的图像分割与边缘检测方法。

一、图像分割方法1. 基于阈值的分割基于阈值的分割是一种简单但有效的方法。

该方法将图像像素的灰度值与预设的阈值进行比较，根据比较结果将像素分配到不同的区域。

在Matlab中，可以使用imbinarize和graythresh函数来实现基于阈值的分割。

2. 区域增长法区域增长法基于像素之间的相似性来进行分割。

该方法从种子像素开始，通过判断邻域像素与种子像素的相似度来不断扩展区域。

在Matlab中，可以使用imsegf和regiongrowing函数来实现区域增长法。

3. 聚类方法聚类方法将图像像素分为多个类别，每个类别代表一个区域。

该方法通常使用聚类算法，比如k-means算法或者模糊c-均值算法。

在Matlab中，可以使用kmeans和fcm函数来实现聚类方法。

4. 模型驱动法模型驱动法基于数学模型来描述图像中的区域。

该方法通过定义一个能够衡量图像中区域特征的能量函数，并通过优化算法来最小化能量函数，从而得到分割结果。

在Matlab中，可以使用activecontour和chanvese函数来实现模型驱动法。

二、边缘检测方法1. Sobel算子Sobel算子是一种经典的边缘检测算子。

其基本思想是通过计算像素与其周围像素之间的差异来检测边缘。

在Matlab中，可以使用imgradient和imgradientxy函数来实现Sobel算子。

2. Canny算子Canny算子是一种广泛使用的边缘检测算子。

它利用高斯平滑、梯度计算、非极大值抑制和双阈值法来检测边缘。

外文翻译---MATLAB 在图像边缘检测中的应用

英文资料翻译MATLAB application in image edge detection MATLAB of the 1984 countries MathWorks company to market since, after 10 years of development, has become internationally recognized the best technology application software. MATLAB is not only a kind of direct, efficient computer language, and at the same time, a scientific computing platform, it for data analysis and data visualization, algorithm and application development to provide the most core of math and advanced graphics tools. According to provide it with the more than 500 math and engineering function, engineering and technical personnel and scientific workers can integrated environment of developing or programming to complete their calculation.MATLAB software has very strong openness and adapt to sex. Keep the kernel in under the condition of invariable, MATLAB is in view of the different application subject of launch corresponding Toolbox (Toolbox), has now launched image processing Toolbox, signal processing Toolbox, wavelet Toolbox, neural network Toolbox and communication tools box, etc multiple disciplines special kit, which would place of different subjects research work.MATLAB image processing kit is by a series of support image processing function from the composition, the support of the image processing operation: geometric operation area of operation and operation; Linear filter and filter design; Transform (DCT transform); Image analysis and strengthened; Binary image manipulation, etc. Image processing tool kit function, the function can be divided into the following categories: image display; Image file input and output; Geometric operation; Pixels statistics; Image analysis and strengthened; Image filtering; Sex 2 d filter design; Image transformation; Fields and piece of operation; Binary image operation; Color mapping and color space transformation; Image types and type conversion; Kit acquiring parameters and Settings.1.Edge detection thisUse computer image processing has two purposes: produce more suitable for human observation and identification of the images; Hope can by the automatic computer image recognition and understanding.No matter what kind of purpose to, image processing the key step is to contain a variety of scenery of decomposition of image information. Decomposition of the end result is that break down into some has some kind of characteristics of the smallest components, known as the image of the yuan. Relative to the whole image of speaking, this the yuan more easily to be rapid processing.Image characteristics is to point to the image can be used as the sign of the field properties, it can be divided into the statistical features of the image and image visual, two types of levy. The statistical features of the image is to point to some people the characteristics of definition, through the transform to get, such as image histogram, moments, spectrum, etc.; Image visual characteristics is refers to person visual sense can be directly by the natural features, such as the brightness of the area, and texture or outline, etc. The two kinds of characteristics of the image into a series of meaningful goal or regional p rocess called image segmentation.The image is the basic characteristics of edge, the edge is to show its pixel grayscale around a step change order or roof of the collection of those changes pixels. It exists in target and background, goals and objectives, regional and region, the yuan and the yuan between, therefore, it is the image segmentation dependent on the most important characteristic that the texture characteristics of important information sources and shape characteristics of the foundation, and the image of the texture characteristics and the extraction of shape often dependent on image segmentation. Image edge extraction is also the basis of image matching, because it is the sign of position, the change of the original is not sensitive, and can be used for matching the feature points.The edge of the image is reflected by gray not continuity. Classic edge extraction method is investigation of each pixel image in an area of the gray change, use edge first or second order nearby directional derivative change rule,with simple method of edge detection, this method called edge detection method of local operators.The type of edge can be divided into two types: (1) step representation sexual edge, it on both sides of the pixel gray value varies significantly different; (2) the roof edges, it is located in gray value from the change of increased to reduce the turning point. For order jump sexual edge, second order directional derivative in edge is zero cross; For the roof edges, second order directional derivative in edge take extreme value.If a pixel fell in the image a certain object boundary, then its field will become a gray level with the change. The most useful to change two features is the rate of change and the gray direction, they are in the range of the gradient vector and the direction to said. Edge detection operator check every pixel grayscale rate fields and evaluation, and also include to determine the directions of the most use based on directional derivative deconvolution method for masking.Digital image processing technique has been widely applied to the biomedical field, the use of computer image processing and analysis, and complete detection and recognition of cancer cells can help doctors make a diagnosis of tumor cancers. Need to be made in the identification of cancer cells, the quantitative results, the human eye is difficult to accurately complete such work, and the use of computer image processing to complete the analysis and identification of the microscopic images have made great progress. In recent years, domestic and foreign medical images of cancer cells testing to identify the researchers put forward a lot of theory and method for the diagnosis of cancer cells has very important meaning and practical value.Cell edge detection is the cell area of the number of roundness and color, shape and chromaticity calculation and the basis of the analysis their test results directly affect the analysis and diagnosis of the disease. Classical edge detection operators such as Sobel operator, Laplacian operator, each pixel neighborhood of the image gray scale changes to detect the edge. Although these operators is simple, fast, but there are sensitive to noise, get isolated or in short sections of acontinuous edge pixels, overlapping the adjacent cell edge defects, while the optimal threshold segmentation and contour extraction method of combining edge detection, obtained by the iterative algorithm for the optimal threshold for image segmentation, contour extraction algorithm, digging inside the cell pixels, the last remaining part of the image is the edge of the cell, change the processing order of the traditional edge detection algorithm, by MATLAB programming, the experimental results that can effectively suppress the noise impact at the same time be able to objectively and correctly select the edge detection threshold, precision cell edge detection.2.Edge detection of MATLABMATLAB image processing toolkit defines the edge () function is used to test the edge of gray image.(1) BW = edge (I, "method"), returns and I size binary image BW, includingelements of 1 said is on the edge of the point, 0 means the edge points.Method for the following a string of:1) soble: the default value, with derivative Sobel edge detectionapproximate measure, to return to a maximum gradient edge;2) prewitt: with the derivative prewitt approximate edge detection, amaximum gradient to return to edge;3) Roberts: with the derivative Roberts approximate edge detection margins,return to a maximum gradient edge;4) the log: use the Laplace operation gaussian filter to I carry filtering,through the looking for 0 intersecting detection of edge;5) zerocross: use the filter to designated I filter, looking for 0 intersectingdetection of edge.(2) BW = edge (I, "method", thresh) with thresh designated sensitivitythreshold value, rather than the edge of all not thresh are ignored.(3) BW = edge (I, "method" thresh, direction, for soble and prewitt methodspecified direction, direction for string, including horizontal level said direction; Vertical said to hang straight party; Both said the two directions(the default).(4) BW = edge (I, 'log', thresh, log sigma), with sigma specified standarddeviation.(5) [BW, thresh] = edge (...), the return value of a function in fact have multiple(" BW "and" thresh "), but because the brace up with u said as a matrix, and so can be thought a return only parameters, which also shows the introduction of the concept of matrix MATLAB unity and superiority.st wordMATLAB has strong image processing function, provide a simple function calls to realize many classic image processing method. Not only is the image edge detection, in transform domain processing, image enhancement, mathematics morphological processing, and other aspects of the study, MATLAB can greatly improve the efficiency rapidly in the study of new ideas.MATLAB 在图像边缘检测中的应用MATLAB自1984年由国MathWorks公司推向市场以来,历经十几年的发展,现已成为国际公认的最优秀的科技应用软件。

关于MATLAB边缘检测sobel算子

关于MATLAB边缘检测sobel算子一、sobel介绍索贝尔算子是图像处理中的算子之一，主要用作边缘检测。

在技术上，它是一离散性差分算子，用来运算图像亮度函数的梯度之近似值。

在图像的任何一点使用此算子，将会产生对应的梯度矢量或是其法矢量。

该算子包含两组3x3的矩阵，分别为横向及纵向，将之与图像作平面卷积，即可分别得出横向及纵向的亮度差分近似值。

如果以代表原始图像，及分别代表经横向及纵向边缘检测的图像，其公式如下:图像的每一个像素的横向及纵向梯度近似值可用以下的公式结合，来计算梯度的大小。

然后可用以下公式计算梯度方向。

在以上例子中，如果以上的角度等于零，即代表图像该处拥有纵向边缘，左方较右方暗。

二、程序例1clear all;close all;f=imread('dsy.jpg');u=rgb2gray(f);F=double(f);U=double(u);[H,W]=size(u);uSobel=u;% ms=0;% ns=0;for i=2:H-1for j=2:W-1Gx=(U(i+1,j-1)+2*U(i+1,j)+F(i+1,j+1))-(U(i-1,j-1)+2*U(i-1,j)+F(i-1,j+1));Gy=(U(i-1,j+1)+2*U(i,j+1)+F(i+1,j+1))-(U(i-1,j-1)+2*U(i,j-1)+F(i+1,j-1));uSobel(i,j)=sqrt(Gx^2+Gy^2);% ms=ms+uSobel(i,j);% ns=ns+(uSobel(i,j)-ms)^2;endend% ms=ms/(H*W);% ns=ns/(H*W);subplot(1,2,1);imshow(f);title('原图');subplot(1,2,2);imshow(im2uint8(uSobel));title('Sobel处理后');% S=[ms ns];程序运行结果：例2hg=zeros(3,3); %设定高斯平滑滤波模板的大小为3*3delta=0.5;for x=1:1:3for y=1:1:3u=x-2;v=y-2;hg(x,y)=exp(-(u^2+v^2)/(2*pi*delta^2));endendh=hg/sum(hg(:));g = imread('jjj.jpg ');f=rgb2gray(im2double(g));subplot(2,2,1),imshow(f)title('原始图像');[m,n]=size(f);ftemp=zeros(m,n);rowhigh=m-1;colhigh=n-1;%%%高斯滤波%%%for x=2:1:rowhigh-1for y=2:1:colhigh-1mod=[f(x-1,y-1) f(x-1,y) f(x-1,y+1); f(x,y-1) f(x,y) f(x,y+1);f(x+1,y-1) f(x+1,y) f(x+1,y+1)];A=h.*mod;ftemp(x,y)=sum(A(:));endendf=ftempsubplot(2,2,2),imshow(f)title('高斯滤波器后的图像');%%%%3*3的soble算子%%%%%%%%sx=[-1 -2 -1;0 0 0;1 2 1];sy=[-1 0 1;-2 0 2;-1 0 1];for x=2:1:rowhigh-1for y=2:1:colhigh-1mod=[f(x-1,y-1) f(x-1,y) f(x-1,y+1); f(x,y-1) f(x,y) f(x,y+1);f(x+1,y-1) f(x+1,y) f(x+1,y+1)];fsx=sx.*mod;fsy=sy.*mod;ftemp(x,y)=max((abs(sum(fsx(:)))),(abs(sum(fsy(:)))));endendfs=im2uint8(ftemp);subplot(2,2,3),imshow(fs)title('用soble检测的原始图像');%%%域值分割%%%TH2=30; %设定阈值for x=2:1:rowhigh-1for y=2:1:colhigh-1if (fs(x,y)>=TH2)&((fs(x,y-1) <= fs(x,y)) & (fs(x,y) > fs(x,y+1)) )fs(x,y)=200;elseif(fs(x,y)>=TH2)&( (fs(x-1,y) <=fs(x,y)) & (fs(x,y) >fs(x+1,y))) fs(x,y)=200;else fs(x,y)=50;endendendsubplot(2,2,4),imshow(fs)title('用soble检测并细化后的图像')。

边缘检测中英文翻译

Digital Image Processing and Edge DetectionDigital Image ProcessingInterest in digital image processing methods stems from two principal application areas: improvement of pictorial information for human interpretation; and processing of image data for storage, transmission, and representation for autonomous machine perception.An image may be defined as a two-dimensional function, f(x,y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x, y) is called the intensity or gray level of the image at that point. When x, y, and the amplitude values of f are all finite, discrete quantities, we call the image a digital image. The field of digital image processing refers to processing digital images by means of a digital computer. Note that a digital image is composed of a finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pels, and pixels. Pixel is the term most widely used to denote the elements of a digital image.Vision is the most advanced of our senses, so it is not surprising that images play the single most important role in human perception. However, unlike humans, who are limited to the visual band of the electromagnetic (EM) spectrum, imaging machines cover almost the entire EM spectrum, ranging from gamma to radio waves. They can operate on images generated by sources that humans are not accustomed to associating with images. These include ultrasound, electron microscopy, and computer generated images. Thus, digital image processing encompasses a wide and varied field of applications.There is no general agreement among authors regarding where image processing stops and other related areas, such as image analysis and computer 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 computers to emulate human vision, including learning and being able to make inferences and take actions based on visual inputs. This area itself is a branch of artificial intelligence(AI) whose objective is to emulate human intelligence. The field of AI is in its earliest stages of infancy in terms of development, with progress having been much slower than originally anticipated. The area of image analysis (also called image understanding) is in between image processing and computer vision.There are no clearcut boundaries in the continuum from image processing at one end to computer vision at the other. However, one useful paradigm is to consider three types of computerized processes in this continuum: low, mid, and highlevel processes. Low-level processes involve primitive operations such as image preprocessing to reduce noise, contrast enhancement, and image sharpening. A low-level process is characterized by the fact that both its inputs and outputs are images. Mid-level processing on images involves tasks such as segmentation (partitioning an image into regions or objects), description of those objects to reduce them to a form suitable for computer processing, and classification (recognition) of individual objects. A midlevel process is characterized by the fact that its inputs generally are images, but its outputs are attributes extracted from those images (e.g., edges, contours, and the identity of individual objects). Finally, higherlevel processing involves “making sense” of an ensemble of recognize d objects, as in image analysis, and, at the far end of the continuum, performing the cognitive functions normally associated with vision.Based on the preceding comments, we see that a logical place of overlap between image processing and image analysis is the area of recognition of individual regions or objects in an image. Thus, what we call in this book digital image processing encompasses processes whose inputs and outputs are images and, in addition, encompasses processes that extract attributes from images, up to and including the recognition of individual objects. As a simple illustration to clarify these concepts, consider the area of automated analysis of text. The processes of acquiring an image of the area containing the text, preprocessing that image, extracting (segmenting) the individual characters, describing the characters in a form suitable for computer processing, and recognizing those individual characters are in the scope of what we call digital image processing in this book. Making sense of the content of the page may be viewed as being in the domain of image analysis and even computer vision, depending on the level of complexity implied by the statement “making sense.” As will become evident shortly, digital image processing, as we have defined it, is used successfully in a broad range of areas of exceptional social and economic value.The areas of application of digital image processing are so varied that some formof organization is desirable in attempting to capture the breadth of this field. One of the simplest ways to develop a basic understanding of the extent of image processing applications is to categorize images according to their source (e.g., visual, X-ray, and so on). The principal energy source for images in use today is the electromagnetic energy spectrum. Other important sources of energy include acoustic, ultrasonic, and electronic (in the form of electron beams used in electron microscopy). Synthetic images, used for modeling and visualization, are generated by computer. In this section we discuss briefly how images are generated in these various categories and the areas in which they are applied.Images based on radiation from the EM spectrum are the most familiar, especially images in the X-ray and visual bands of the spectrum. Electromagnetic waves can be conceptualized as propagating sinusoidal waves of varying wavelengths, or they can be thought of as a stream of massless particles, each traveling in a wavelike pattern and moving at the speed of light. Each massless particle contains a certain amount (or bundle) of energy. Each bundle of energy is called a photon. If spectral bands are grouped according to energy per photon, we obtain the spectrum shown in fig. below, ranging from gamma rays (highest energy) at one end to radio waves (lowest energy) at the other. The bands are shown shaded to convey the fact that bands of the EM spectrum are not distinct but rather transition smoothly from one to the other.Fig1Image acquisition is the first process. Note that acquisition could be as simple as being given an image that is already in digital form. Generally, the image acquisition stage involves preprocessing, such as scaling.Image enhancement is among the simplest and most appealing areas of digital image processing. Basically, the idea behind enhancement techniques is to bring out detail that is obscured, or simply to highlight certain features of interest in an image.A familiar example of enhancement is when we increase the contrast of an imagebecause “it looks better.” It is important to keep in mind that enhancement is a very subjective area of image processing. Image restoration is an area that also deals with improving the appearance of an image. However, unlike enhancement, which is subjective, image restoration is objective, in the sense that restoration techniques tend to be based on mathematical or probabilistic models of image degradation. Enhancement, on the other hand, is based on human subjective preferences regarding what constitutes a “good” en hancement result.Color image processing is an area that has been gaining in importance because of the significant increase in the use of digital images over the Internet. It covers a number of fundamental concepts in color models and basic color processing in a digital domain. Color is used also in later chapters as the basis for extracting features of interest in an image.Wavelets are the foundation for representing images in various degrees of resolution. In particular, this material is used in this book for image data compression and for pyramidal representation, in which images are subdivided successively into smaller regions.F ig2Compression, as the name implies, deals with techniques for reducing the storage required to save an image, or the bandwidth required to transmi it.Although storagetechnology has improved significantly over the past decade, the same cannot be said for transmission capacity. This is true particularly in uses of the Internet, which are characterized by significant pictorial content. Image compression is familiar (perhaps inadvertently) to most users of computers in the form of image file extensions, such as the jpg file extension used in the JPEG (Joint Photographic Experts Group) image compression standard.Morphological processing deals with tools for extracting image components that are useful in the representation and description of shape. The material in this chapter begins a transition from processes that output images to processes that output image attributes.Segmentation procedures partition an image into its constituent parts or objects. In general, autonomous segmentation is one of the most difficult tasks in digital image processing. A rugged segmentation procedure brings the process a long way toward successful solution of imaging problems that require objects to be identified individually. On the other hand, weak or erratic segmentation algorithms almost always guarantee eventual failure. In general, the more accurate the segmentation, the more likely recognition is to succeed.Representation and description almost always follow the output of a segmentation stage, which usually is raw pixel data, constituting either the boundary of a region (i.e., the set of pixels separating one image region from another) or all the points in the region itself. In either case, converting the data to a form suitable for computer processing is necessary. The first decision that must be made is whether the data should be represented as a boundary or as a complete region. Boundary representation is appropriate when the focus is on external shape characteristics, such as corners and inflections. Regional representation is appropriate when the focus is on internal properties, such as texture or skeletal shape. In some applications, these representations complement each other. Choosing a representation is only part of the solution for transforming raw data into a form suitable for subsequent computer processing. A method must also be specified for describing the data so that features of interest are highlighted. Description, also called feature selection, deals with extracting attributes that result in some quantitative information of interest or are basic for differentiating one class of objects from another.Recognition is the pro cess that assigns a label (e.g., “vehicle”) to an object based on its descriptors. As detailed before, we conclude our coverage of digital imageprocessing with the development of methods for recognition of individual objects.So far we have said nothing about the need for prior knowledge or about the interaction between the knowledge base and the processing modules in Fig2 above. Knowledge about a problem domain is coded into an image processing system in the form of a knowledge database. This knowledge may be as simple as detailing regions of an image where the information of interest is known to be located, thus limiting the search that has to be conducted in seeking that information. The knowledge base also can be quite complex, such as an interrelated list of all major possible defects in a materials inspection problem or an image database containing high-resolution satellite images of a region in connection with change-detection applications. In addition to guiding the operation of each processing module, the knowledge base also controls the interaction between modules. This distinction is made in Fig2 above by the use of double-headed arrows between the processing modules and the knowledge base, as opposed to single-headed arrows linking the processing modules.Edge detectionEdge detection is a terminology in image processing and computer vision, particularly in the areas of feature detection and feature extraction, to refer to algorithms which aim at identifying points in a digital image at which the image brightness changes sharply or more formally has discontinuities.Although point and line detection certainly are important in any discussion on segmentation,edge dectection is by far the most common approach for detecting meaningful discounties in gray level.Although certain literature has considered the detection of ideal step edges, the edges obtained from natural images are usually not at all ideal step edges. Instead they are normally affected by one or several of the following effects:1.focal b lur caused by a finite depth-of-field and finite point spread function; 2.penumbral blur caused by shadows created by light sources of non-zero radius; 3.shading at a smooth object edge; 4.local specularities or interreflections in the vicinity of object edges.A typical edge might for instance be the border between a block of red color and a block of yellow. In contrast a line (as can be extracted by a ridge detector) can be a small number of pixels of a different color on an otherwise unchanging background. For a line, there may therefore usually be one edge on each side of the line.To illustrate why edge detection is not a trivial task, let us consider the problemof detecting edges in the following one-dimensional signal. Here, we may intuitively say that there should be an edge between the 4th and 5th pixels.If the intensity difference were smaller between the 4th and the 5th pixels and if the intensity differences between the adjacent neighbouring pixels were higher, it would not be as easy to say that there should be an edge in the corresponding region. Moreover, one could argue that this case is one in which there are several edges.Hence, to firmly state a specific threshold on how large the intensity change between two neighbouring pixels must be for us to say that there should be an edge between these pixels is not always a simple problem. Indeed, this is one of the reasons why edge detection may be a non-trivial problem unless the objects in the scene are particularly simple and the illumination conditions can be well controlled.There are many methods for edge detection, but most of them can be grouped into two categories,search-based and zero-crossing based. The search-based methods detect edges by first computing a measure of edge strength, usually a first-order derivative expression such as the gradient magnitude, and then searching for local directional maxima of the gradient magnitude using a computed estimate of the local orientation of the edge, usually the gradient direction. The zero-crossing based methods search for zero crossings in a second-order derivative expression computed from the image in order to find edges, usually the zero-crossings of the Laplacian or the zero-crossings of a non-linear differential expression, as will be described in the section on differential edge detection following below. As a pre-processing step to edge detection, a smoothing stage, typically Gaussian smoothing, is almost always applied (see also noise reduction).The edge detection methods that have been published mainly differ in the types of smoothing filters that are applied and the way the measures of edge strength are computed. As many edge detection methods rely on the computation of image gradients, they also differ in the types of filters used for computing gradient estimates in the x- and y-directions.Once we have computed a measure of edge strength (typically the gradient magnitude), the next stage is to apply a threshold, to decide whether edges are present or not at an image point. The lower the threshold, the more edges will be detected, and the result will be increasingly susceptible to noise, and also to picking outirrelevant features from the image. Conversely a high threshold may miss subtle edges, or result in fragmented edges.If the edge thresholding is applied to just the gradient magnitude image, the resulting edges will in general be thick and some type of edge thinning post-processing is necessary. For edges detected with non-maximum suppression however, the edge curves are thin by definition and the edge pixels can be linked into edge polygon by an edge linking (edge tracking) procedure. On a discrete grid, the non-maximum suppression stage can be implemented by estimating the gradient direction using first-order derivatives, then rounding off the gradient direction to multiples of 45 degrees, and finally comparing the values of the gradient magnitude in the estimated gradient direction.A commonly used approach to handle the problem of appropriate thresholds for thresholding is by using thresholding with hysteresis. This method uses multiple thresholds to find edges. We begin by using the upper threshold to find the start of an edge. Once we have a start point, we then trace the path of the edge through the image pixel by pixel, marking an edge whenever we are above the lower threshold. We stop marking our edge only when the value falls below our lower threshold. This approach makes the assumption that edges are likely to be in continuous curves, and allows us to follow a faint section of an edge we have previously seen, without meaning that every noisy pixel in the image is marked down as an edge. Still, however, we have the problem of choosing appropriate thresholding parameters, and suitable thresholding values may vary over the image.Some edge-detection operators are instead based upon second-order derivatives of the intensity. This essentially captures the rate of change in the intensity gradient. Thus, in the ideal continuous case, detection of zero-crossings in the second derivative captures local maxima in the gradient.We can come to a conclusion that,to be classified as a meaningful edge point,the transition in gray level associated with that point has to be significantly stronger than the background at that point.Since we are dealing with local computations,the method of choice to determine whether a value is “significant” or not id to use a threshold.Thus we define a point in an image as being as being an edge point if its two-dimensional first-order derivative is greater than a specified criterion of connectedness is by definition an edge.The term edge segment generally is used if the edge is short in relation to the dimensions of the image.A key problem insegmentation is to assemble edge segments into longer edges.An alternate definition if we elect to use the second-derivative is simply to define the edge ponits in an image as the zero crossings of its second derivative.The definition of an edge in this case is the same as above.It is important to note that these definitions do not guarantee success in finding edge in an image.They simply give us a formalism to look for them.First-order derivatives in an image are computed using the gradient.Second-order derivatives are obtained using the Laplacian.数字图像处理与边缘检测数字图像处理数字图像处理方法的研究源于两个主要应用领域：其一是改进图像信息以便于人们分析；其二是为使机器自动理解而对图像数据进行存储、传输及显示。

matlab中translate

matlab中translate
在MATLAB中，"translate"通常指的是对图形或图像进行平移操作。

平移是指将图形沿着指定的方向和距离移动。

在MATLAB中，可以使用内置函数来执行平移操作。

例如，使用imtranslate函数可以对图像进行平移操作，语法为imtranslate(A, tform)，其中A 是输入图像，tform是一个仿射变换对象，指定了平移的距离和方向。

除了图形和图像的平移，"translate"也可以指代对文本或字符串的翻译操作。

MATLAB提供了一些字符串处理函数，可以用于文本的翻译和转换。

例如，可以使用strrep函数来替换字符串中的特定文本，实现简单的翻译功能。

另外，MATLAB还提供了对文本进行翻译的工具箱，可以实现更复杂的多语言翻译和处理。

此外，在MATLAB中还有一些其他领域的“translate”操作，比如信号处理中的信号平移、控制系统中的系统状态转移等。

针对不同的领域和需求，MATLAB提供了丰富的工具和函数，可以实现各种类型的“translate”操作。

总之，在MATLAB中，“translate”可以涉及图形、图像、文
本、信号处理、控制系统等多个领域，针对不同的应用场景，可以使用不同的函数和工具实现相应的平移或转换操作。

希望这个回答能够全面地解答你的问题。

matlab sobel函数 -回复

matlab sobel函数-回复使用MATLAB中的Sobel函数进行边缘检测在图像处理中，边缘检测是一种常用的技术，用于检测图像中物体的边界。

边缘检测是图像处理中的一个关键步骤，通常在图像分析、计算机视觉和机器学习任务中被广泛应用。

MATLAB是一种功能强大的数值计算和数据可视化软件，它提供了许多用于图像处理的函数，其中包括用于边缘检测的Sobel函数。

Sobel算子是一种常用的边缘检测算法，可以通过计算图像中的强度梯度来检测边缘。

在这篇文章中，我们将一步一步地介绍如何使用MATLAB中的Sobel函数进行边缘检测。

第一步：读取图像首先，我们需要载入要进行边缘检测的图像。

在MATLAB中，可以使用imread函数读取图片。

例如，我们可以使用以下代码读取一个名为“image.jpg”的图像文件：matlabimage = imread('image.jpg');第二步：将图像转换为灰度图像Sobel算子只能应用于灰度图像，因此在应用Sobel算子之前，我们需要将图像转换为灰度图像。

幸运的是，MATLAB提供了灵活的函数来完成这个任务。

我们可以使用rgb2gray函数将彩色图像转换为灰度图像：matlabgrayImage = rgb2gray(image);第三步：应用Sobel算子现在，我们已经将图像转换为灰度图像，接下来我们可以使用MATLAB 中的Sobel函数对图像应用Sobel算子。

Sobel函数可以通过计算图像中每个像素的梯度来检测边缘。

Sobel函数的基本语法如下：matlabsobelImage = edge(grayImage, 'Sobel');在这个例子中，grayImage是我们要进行边缘检测的灰度图像，'Sobel'表示我们要使用Sobel算子进行边缘检测。

第四步：显示结果完成边缘检测后，我们可以使用MATLAB中的imshow函数显示结果图像。

matlab边缘检测代码

MATLAB边缘检测代码边缘检测是图像处理中常用的技术，用于识别图像中物体的轮廓。

在MATLAB中，我们可以使用不同的方法进行边缘检测，例如Sobel算子、Canny算子等。

本文将介绍MATLAB中常用的边缘检测方法，并给出相应的代码示例。

1. Sobel算子Sobel算子是一种基于梯度的边缘检测算法，通过计算图像灰度值的一阶导数来识别边缘。

在MATLAB中，我们可以使用edge函数来实现Sobel算子。

img = imread('image.jpg'); % 读取图像gray_img = rgb2gray(img); % 将彩色图像转换为灰度图像edge_img = edge(gray_img, 'sobel'); % 使用Sobel算子进行边缘检测imshow(edge_img); % 显示结果上述代码首先读取一张彩色图像，并将其转换为灰度图像。

然后使用edge函数对灰度图像进行Sobel边缘检测，并将结果显示出来。

2. Canny算子Canny算子是一种基于多阶段处理的边缘检测算法，它能够有效地抑制噪声并提取出清晰、准确的边缘。

在MATLAB中，我们同样可以使用edge函数来实现Canny算子。

img = imread('image.jpg'); % 读取图像gray_img = rgb2gray(img); % 将彩色图像转换为灰度图像edge_img = edge(gray_img, 'canny'); % 使用Canny算子进行边缘检测imshow(edge_img); % 显示结果上述代码与Sobel算子的示例代码类似，只是将edge函数的第二个参数设置为'canny'来使用Canny算子进行边缘检测。

3. Laplacian算子Laplacian算子是一种基于二阶导数的边缘检测算法，它能够检测出图像中的灰度变化区域。

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.。

Matlab在CAD边缘检测处理中的应用

与识别首要解决的经典难题，也是制约医学图像处理中其他
相关技术发展和应用的瓶颈［。近几十年来人们提出了很多２］图像分割方法，如基于阈值分割方法、基于边缘检测分割方法和基于区域分割方法，以及基于模式识别理论分割方法和数学形态学分割方法等。本研究在经典边缘检测分割方法基础
药物的溶出是一个极其复杂的动力学过程，难实现用很单一的数据模型来解决实际问题，其是在溶出实验点较多尤的情况下。因此，应进行多种模型拟合并按照一定的模型拟合优劣评价指标，如决定系数Ｒ。赤池信息准则（ａｋＩ— 、ＡｋｉｅｎＳ
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算法的图像分割新方法，可以使感兴趣区轮廓线清晰地显示，改善了医生诊断效果。关键词：ＭＡＴＬＣＡＢ；ＡＤ；边缘检测；形态学图像处理
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sobel算子检测边缘 matlab编程

在MATLAB中，使用Sobel算子检测图像边缘的一个基本例子可以如下：matlab复制代码% 读取图像I = imread('your_image.jpg');% 转换为灰度图像I_gray = rgb2gray(I);% 使用Sobel算子进行边缘检测BW = edge(I_gray,'sobel');% 显示原图像和边缘检测后的图像figure,subplot(1,2,1), imshow(I), title('原图像')subplot(1,2,2), imshow(BW), title('Sobel边缘检测后的图像')在上述代码中，首先读取一个图像，然后将其转换为灰度图像。

然后，使用MATLAB 内置的edge函数，指定'sobel'作为方法参数，对灰度图像进行边缘检测。

最后，显示原始图像和经过Sobel边缘检测后的图像。

注意：你需要将'your_image.jpg'替换为你想要处理的图像的文件名。

如果该文件不在MATLAB的当前工作目录中，你需要提供完整的文件路径。

如果你想自己实现Sobel算子而不是使用内置的edge函数，你可以创建一个Sobel算子，然后将其应用于图像。

这是一个基本的例子：matlab复制代码% 读取图像I = imread('your_image.jpg');% 转换为灰度图像I_gray = rgb2gray(I);% 定义Sobel算子Gx = [-101; -202; -101]; % x方向Gy = [121; 000; -1-2-1]; % y方向% 计算图像大小[height, width] = size(I_gray);% 初始化输出图像Ix = zeros(height-2, width-2);Iy = zeros(height-2, width-2);I_edge = zeros(height-2, width-2);% 应用Sobel算子for i = 2:height-1for j = 2:width-1Ix(i-1,j-1) = sum(sum(double(I_gray(i-1:i+1,j-1:j+1)) .* Gx));Iy(i-1,j-1) = sum(sum(double(I_gray(i-1:i+1,j-1:j+1)) .* Gy));% 计算梯度幅度I_edge(i-1,j-1) = sqrt(Ix(i-1,j-1)^2 + Iy(i-1,j-1)^2);endend% 二值化图像BW = I_edge > threshold; % threshold是一个阈值，你需要自己设定% 显示原图像和边缘检测后的图像figure,subplot(1,2,1), imshow(I), title('原图像')subplot(1,2,2), imshow(BW), title('Sobel边缘检测后的图像')注意：在这个例子中，你需要自己设定一个阈值（threshold）来决定哪些像素被视为边缘。

Matlab技术图像分割与边缘检测

Matlab技术图像分割与边缘检测图像处理是计算机领域中一个重要的研究方向，其中图像分割和边缘检测是非常基础且关键的技术。

而Matlab作为一种强大的数学软件工具，提供了丰富的函数和算法来实现图像处理任务。

本文将介绍Matlab中的技术图像分割和边缘检测方法，并探讨其应用。

图像分割是将一张图像划分成多个独立的区域或对象的过程，旨在提取出图像中的关键信息。

在Matlab中，有多种图像分割算法可供选择，常见的有阈值分割、区域生长和边缘检测等。

阈值分割是最简单且常用的一种图像分割方法。

它基于图像的像素灰度值，将图像分为不同的区域。

在Matlab中，使用函数im2bw实现二值化操作，可以根据指定的阈值将图像转化为二值图像。

阈值的选取对分割结果影响很大，需要根据具体应用来确定。

区域生长是另一种常见的图像分割方法。

它从一个种子点开始，将与其邻近像素满足一定条件的像素逐步加入到同一区域中。

在Matlab中，使用函数regiongrowing实现区域生长操作。

该方法对图像的灰度变化敏感，适用于分割具有明显灰度差异的图像。

边缘检测是用于提取图像中物体轮廓的一种技术。

图像的边缘通常包括物体的外轮廓和内部的纹理和细节。

在Matlab中，有多种边缘检测算法可供选择，常见的有Sobel算子、Canny算子和拉普拉斯算子等。

Sobel算子是一种经典的边缘检测算法，它基于图像的灰度变化率来检测边缘。

在Matlab中，使用函数edge实现Sobel算子的操作。

Sobel算子对噪声有较好的抑制能力，适用于对噪声较多的图像进行边缘检测。

Canny算子是一种广泛应用的边缘检测算法，它综合考虑了边缘检测的准确性、灵敏度和抑制噪声的能力。

在Matlab中，同样使用函数edge实现Canny算子的操作。

Canny算子的主要步骤包括高斯滤波、计算梯度、非最大抑制和阈值处理等。

拉普拉斯算子是一种基于图像二阶导数的边缘检测算法，它能够提取出图像中的细节和纹理信息。

matlab edge函数用法

matlab edge函数用法
```matlab
BW = edge(I, method)
```
其中，I 是输入图像，method 是边缘检测方法的名称。

以下是一些常用的边缘检测方法以及它们在 edge 函数中的使用方式：
- Roberts 方法：这是一种基于差分的边缘检测算法，通过计算图像中相邻像素之间的差异来检测边缘。

在 edge 函数中，可以使用字符串'Roberts'来指定使用 Roberts 方法。

- Sobel 和 Prewitt 方法：可以检测垂直方向和/或水平方向上的边缘。

在 edge 函数中，可以使用字符串'Sobel'或'Prewitt'来指定使用这两种方法。

在使用 edge 函数时，需要根据具体的需求和应用场景选择合适的边缘检测方法，并调整相关参数以获得最佳的效果。

如果你需要进一步了解边缘检测的原理和应用，请查阅相关文献和资料。

图像边缘检测算法英文文献翻译中英文翻译

image edge examination algorithmAbstractDigital image processing took a relative quite young discipline, is following the computer technology rapid development, day by day obtains the widespread edge took the image one kind of basic characteristic, in the pattern recognition, the image division, the image intensification as well as the image compression and so on in the domain has a more widespread edge detection method many and varied, in which based on brightness algorithm, is studies the time to be most long, the theory develops the maturest method, it mainly is through some difference operator, calculates its gradient based on image brightness the change, thus examines the edge, mainly has Robert, Laplacian, Sobel, Canny, operators and so on LOG。

First as a whole introduced digital image processing and the edge detection survey, has enumerated several kind of at present commonly used edge detection technology and the algorithm, and selects two kinds to use Visual the C language programming realization, through withdraws the image result to two algorithms the comparison, the research discusses their good and bad points.ForewordIn image processing, as a basic characteristic, the edge of the image, which is widely used in the recognition, segmentation,intensification and compress of the image, is often applied to high-level are many kinds of ways to detect the edge. Anyway, there are two main techniques: one is classic method based on the gray grade of every pixel; the other one is based on wavelet and its multi-scale characteristic. The first method, which is got the longest research,get the edge according to the variety of the pixel gray. The main techniques are Robert, Laplace, Sobel, Canny and LOG algorithm.The second method, which is based on wavelet transform, utilizes the Lipschitz exponent characterization of the noise and singular signal and then achieve the goal of removing noise and distilling the real edge lines. In recent years, a new kind of detection method, which based on the phase information of the pixel, is developed. We need hypothesize nothing about images in advance. The edge is easy to find in frequency domain. It’s a reliable method.In chapter one, we give an overview of the image edge. And in chapter two, some classic detection algorithms are introduced. The cause of positional error is analyzed, and then discussed a more precision method in edge orientation. In chapter three, wavelet theory is introduced. The detection methods based on sampling wavelet transform, which can extract maim edge of the image effectively, and non-sampling wavelet transform, which can remain the optimum spatial information, are recommended respectively. In the last chapter of this thesis, the algorithm based on phase information is introduced. Using the log Gabor wavelet, two-dimension filter is constructed, many kinds of edges are detected, including Mach Band, which indicates it is a outstanding and bio-simulation method。

robert算子边缘检测matlab代码

robert算子边缘检测matlab代码罗伯特（Roberts）算子是一种边缘检测算子，用于检测图像中的边缘。

以下是一个简单的MATLAB代码示例，用于应用罗伯特算子进行边缘检测：```matlab%读取图像originalImage=imread('lena.jpg');%请替换成你的图像文件路径%将图像转换为灰度图grayImage=rgb2gray(originalImage);%罗伯特算子边缘检测robertsX=[10;0-1];robertsY=[01;-10];%使用卷积进行边缘检测edgeX=conv2(double(grayImage),robertsX,'same');edgeY=conv2(double(grayImage),robertsY,'same');%计算边缘强度edgeMagnitude=sqrt(edgeX.^2+edgeY.^2);%显示结果figure;subplot(2,2,1),imshow(originalImage),title('原始图像');subplot(2,2,2),imshow(grayImage),title('灰度图');subplot(2,2,3),imshow(edgeMagnitude,[]),title('罗伯特算子边缘检测结果');subplot(2,2,4),imshowpair(edgeX,edgeY,'montage'),title('X方向和Y方向边缘');%设置图像标题suptitle('罗伯特算子边缘检测');```请确保将`'lena.jpg'`替换为你实际使用的图像文件路径。

这个代码使用了罗伯特算子的卷积核进行水平（X方向）和垂直（Y方向）方向的边缘检测，然后计算了边缘强度，并显示了原始图像、灰度图和边缘检测结果。

matlab-图像处理-边缘检测算法五种

matlab-图像处理-边缘检测算法五种五种实现matlab边缘检测算法：⽅法⼀：matlab⾃带的edge函数：将图⽚保存为lena.jpgI=imread('lena.jpg');%提取图像img=rgb2gray(I);[m,n]=size(img);BW1=edge(img,'sobel'); %⽤Sobel算⼦进⾏边缘检测BW2=edge(img,'roberts');%⽤Roberts算⼦进⾏边缘检测BW3=edge(img,'prewitt'); %⽤Prewitt算⼦进⾏边缘检测BW4=edge(img,'log'); %⽤Log算⼦进⾏边缘检测BW5=edge(img,'canny'); %⽤Canny算⼦进⾏边缘检测h=fspecial('gaussian',5);%?⾼斯滤波BW6=edge(img,'canny');%⾼斯滤波后使⽤Canny算⼦进⾏边缘检测subplot(2,3,1), imshow(BW1);title('sobel edge check');subplot(2,3,2), imshow(BW2);title('roberts edge check');subplot(2,3,3), imshow(BW3);title('prewitt edge check');subplot(2,3,4), imshow(BW4);title('log edge check');subplot(2,3,5), imshow(BW5);title('canny edge check');subplot(2,3,6), imshow(BW6);title('gasussian&canny edge check');　效果如下图所⽰：⽅法⼆：Laplacian算法clear;sourcePic=imread('lena.jpg');%图像读⼊grayPic=mat2gray(sourcePic);%实现图像的矩阵归⼀化操作[m,n]=size(grayPic);newGrayPic=grayPic;LaplacianNum=0;%经Laplacian操作得到的每个像素的值LaplacianThreshold=0.2;%设定阈值for j=2:m-1 %进⾏边界提取for k=2:n-1LaplacianNum=abs(4*grayPic(j,k)-grayPic(j-1,k)-grayPic(j+1,k)-grayPic(j,k+1)-grayPic(j,k-1));if(LaplacianNum > LaplacianThreshold)newGrayPic(j,k)=255;elsenewGrayPic(j,k)=0;endendendfigure,imshow(newGrayPic);title('Laplacian算⼦的处理结果') 效果图如下：⽅法三：Prewitt算法%Prewitt 算⼦的实现：clear;sourcePic=imread('lena.jpg');grayPic=mat2gray(sourcePic);[m,n]=size(grayPic);newGrayPic=grayPic;PrewittNum=0;PrewittThreshold=0.5;%设定阈值for j=2:m-1 %进⾏边界提取for k=2:n-1PrewittNum=abs(grayPic(j-1,k+1)-grayPic(j+1,k+1)+grayPic(j-1,k)-grayPic(j+1,k)+grayPic(j-1,k-1)-grayPic(j+1,k-1))+abs(grayPic(j-1,k+1)+grayPic(j,k+1)+grayPic(j+1,k+1)-grayPic(j-1,k-1)-grayPic(j,k-1)-grayPic(j+1,k-1)); if(PrewittNum > PrewittThreshold)newGrayPic(j,k)=255;elsenewGrayPic(j,k)=0;endendendfigure,imshow(newGrayPic);title('Prewitt算⼦的处理结果') 效果图如下：⽅法四：Sobel算法%Sobel 算⼦的实现：clear;sourcePic=imread('lena.jpg');grayPic=mat2gray(sourcePic);[m,n]=size(grayPic);newGrayPic=grayPic;sobelNum=0;sobelThreshold=0.7;for j=2:m-1for k=2:n-1sobelNum=abs(grayPic(j-1,k+1)+2*grayPic(j,k+1)+grayPic(j+1,k+1)-grayPic(j-1,k-1)-2*grayPic(j,k-1)-grayPic(j+1,k-1))+abs(grayPic(j-1,k-1)+2*grayPic(j-1,k)+grayPic(j-1,k+1)-grayPic(j+1,k-1)-2*grayPic(j+1,k)-grayPic(j+1,k+1)); if(sobelNum > sobelThreshold)newGrayPic(j,k)=255;elsenewGrayPic(j,k)=0;endendendfigure,imshow(newGrayPic);title('Sobel算⼦的处理结果') 效果如下：⽅法五：Roberts 算⼦的实现%Roberts 算⼦的实现：clear all;clc;sourcePic=imread('lena.jpg');grayPic=mat2gray(sourcePic);[m,n]=size(grayPic);newGrayPic=grayPic;robertsNum=0;robertThreshold=0.2;for j=1:m-1for k=1:n-1robertsNum = abs(grayPic(j,k)-grayPic(j+1,k+1)) + abs(grayPic(j+1,k)-grayPic(j,k+1)); if(robertsNum > robertThreshold)newGrayPic(j,k)=255;elsenewGrayPic(j,k)=0;endendendfigure,imshow(newGrayPic);title('roberts算⼦的处理结果') 效果图：。

基于matlab的图像边缘检测原理及应用

目录一．前言----------------------------------------- 二．边缘检测的与提取-----------------------1.边缘检测的定义---------------------------2.图像边缘检测算法的研究内容---------3.边缘检测算子------------------------------3.1.Sobel算子-----------------------------3.2.Canny算子----------------------------4.基于Matlab的实验结果与分析--------- 三．图像边缘检测的应用---------------------一.前言在实际图像边缘检测问题中，图像的边缘作为图像的一种基本特征，经常被应用到较高层次的图像应用中去。

它在图像识别，图像分割，图像增强以及图像压缩等的领域中有较为广泛的应用，也是它们的基础。

图像边缘是图像最基本的特征之一，往往携带着一幅图像的大部分信息。

而边缘存在于图像的不规则结构和不平稳现象中，也即存在于信号的突变点处，这些点给出了图像轮廓的位置，这些轮廓常常是我们在图像边缘检测时所需要的非常重要的一些特征条件，这就需要我们对一幅图像检测并提取出它的边缘。

而边缘检测算法则是图像边缘检测问题中经典技术难题之一，它的解决对于我们进行高层次的特征描述、识别和理解等有着重大的影响；又由于边缘检测在许多方面都有着非常重要的使用价值，所以人们一直在致力于研究和解决如何构造出具有良好性质及好的效果的边缘检测算子的问题。

该课程设计具体考察了两种最常用的边缘检测算子并运用MATLAB进行图像处理比较。

二.边缘检测于算子1.边缘检测的定义图像边缘是图像最基本的特征，边缘在图像分析中起着重要的用。

所谓边缘（edge）是指图像局部特征的不连续性。

灰度或结构信息的突变称为边缘，例如：灰度级的突变、颜色的突变、纹理结的突变。

MATLAB在图像边缘检测中的应用

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其中ｆｘｙ是具有整数像素坐标的输入图像，方根运算使该处（，）平函数组成的，支持的图像处理操作有：何操作、域操作和块操理类似于在人类视觉系统中发生的过程所几区作；性滤波和滤波器设计；线变（）ｉ。２Ｋｒｈ算子它有八个卷积核ｓ
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matlab sobel函数

一、介绍MATLAB Sobel函数MATLAB中的Sobel函数是图像处理工具箱中常用的函数之一，它主要用于边缘检测。

Sobel算子是一种常用的边缘检测算子，可以帮助我们找到图像中的边缘，对图像进行分割和识别等操作起到了至关重要的作用。

在MATLAB中，我们可以通过调用Sobel函数来实现对图像的边缘检测，以及其他相关的图像处理操作。

二、Sobel算子的原理Sobel算子是一种离散型的微分算子，用于检测图像中的边缘。

它通过对图像中每个像素点的灰度值进行加权求和，来获取该像素点的梯度值，并在图像中标记出边缘。

Sobel算子通常使用3x3的模板来进行计算，分为水平和垂直两个方向，分别对图像进行卷积操作。

水平方向的Sobel算子可以帮助我们检测图像中的垂直边缘，而垂直方向的Sobel算子可以帮助我们检测图像中的水平边缘。

三、MATLAB中Sobel函数的基本用法在MATLAB中，我们可以通过调用Sobel函数来实现对图像的边缘检测。

Sobel函数的基本语法如下：```BW = edge(I,'sobel');```其中，I代表输入的灰度图像，'sobel'表示使用Sobel算子进行边缘检测。

调用Sobel函数后，将得到一个二值化的图像BW，其中边缘像素被标记为1，非边缘像素被标记为0。

除了基本的边缘检测之外，Sobel函数还可以通过指定阈值来进行边缘强度的筛选，以及指定方向来进行特定方向的边缘检测。

例如：```BW = edge(I,'sobel',threshold,direction);```其中，threshold表示设定的阈值，direction表示指定的方向。

通过这种方式，我们可以根据具体需求来定制化Sobel函数的边缘检测操作。

四、Sobel算子在图像处理中的应用Sobel算子作为一种经典的边缘检测算子，在图像处理领域有着广泛的应用。

其主要应用包括但不限于以下几个方面：1. 物体识别使用Sobel算子进行边缘检测可以帮助我们找到图像中的物体轮廓，从而实现对物体的自动识别和定位。