MATLAB在图像边缘检测中的应用外文翻译论文.docx

英文资料翻译matlabapplicationinimage edge detectionmatlab 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. 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 process 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 a continuous 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. 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, including elements 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 detection approximate measure, to return to a maximum gradient edge;2) prewitt: with the derivative prewitt approximate edge detection, a maximum 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 intersecting detection of edge.(2) bw = edge (i, method, thresh) with thresh designated sensitivity threshold value, rather than the edge of all not thresh are ignored.(3) bw = edge (i, method thresh, direction, for soble and prewitt method specified 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 standard deviation.(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. last 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。matlab既是一种直观、高效的计算机语言,同时又是一个科学计算平台,它为数据分析和数据可视化、算法和应用程序开发提供了最核心的数学和高级图形工具。根据它提供的500多个数学和工程函数, 工程技术人员和科学工作者可以在它的集成环境中交互或编程以完成各自的计算。 matlab软件具有很强的开放性和适应 性。在保持内核不变的情况下,matlab 可以针对不同的应用学科推出相应的工具箱(toolbox),目前已经推出了图像处理工具箱、信号处理工具箱、小波工具箱、神经网络工具箱以及通信工具 箱等多个学科的专用工具箱,极大地方便了不同学科的研究工作。matlab的图像处理工具包是由一系列支持图像处理操作的函数组成的,所支持的图像处理操作有:几何操作区域操作和块操作;线性滤波和滤波器设计;变换(dct变换);图像分析和增强;二值图像操作等。图像处理工具包的函数,按功能可以分为以下几类:图像显示;图像文件输入与输出;几何操作;像素值统计;图像分析与增强;图像滤波; 性二维滤波器设计;图像变换;领域和块操作;二值图像操作;颜色映射和颜色空间转换;图像类型和类型转换;工具包参数获取和设置等。与其他工具包一样,用户还可以根据需要书写自己的 函数,以满足特定的需要,也可以将这个工具包和信号处理工具包或小波工具包等其他工具包联合起来使用。 边缘检测概述利用计算机进行图像处理有两个目的:产生更适合人类观察和识别的图像;希望能由计算机自动识别和理解图像。无论为了哪种目的,图像处理中关键的一步就是对包含有大量各式各样景物信息的图像进行分解。分解的最终结果是被分解成一些具有某种特征的最小成分,称为图像的基元。相对于整幅图像来说,这种基元更容易被快速处理。图像的特征是指图像场中可用作标志的属性,它可以分为图像的统计特征和图像的视觉特 征两类。图像的统计特征是指一些人为定义的特征，通过变换才能得到,如图像的直方图、矩、频谱等;图像的视觉特征是指人的视觉可直接感 受到的自然特征,如区域的亮度、纹理或轮廓等。利用这两类特征把图像分解成一系列有意义的目标或区域的过程称为图像的分割。图像最基本的特征是边缘,所谓边缘是指其周围像素灰度有阶跃变化或屋顶变化的那些像素的集合。它存在于目标与背景、目标与目标、区域与区域、基元与基元之间,因此,它是图像分割所依赖的最重要的特征,也是纹理特征的重要信息源和形状特征的基础,而图像的纹理形状特征的提取又常常要依赖于图像分割。图像的边缘提取也是图像匹配的基础,因为它是位置的标志,对灰度的变化不敏感,可作为匹配的特征点。图像的边缘是由灰度不连续性所反映的。经典的边缘提取方法是考察图像的每个像素在某个区域内灰度的变化,利用边缘邻近一阶或二阶方向导数变化规律,用简单的方法检测边缘,这种方法称为边缘检测局部算子法。边缘的种类可以分为两种：阶跃性边缘,它两边的像素的灰度值有显著的不同；屋顶状边缘,它位于灰度值从增加到减少的变化转折点。对于阶跃性边缘，二阶方向导数在边缘处呈零交叉；对于屋顶状边缘，二阶方向导数在边缘处取极值。如果一个像素落在图像中某一个物体的边界上,那么它的领域将成为一个灰度级的变化带。对这种变化最有用的两个特征是灰度的变化率和方向,它们分别以梯度向量的幅度和方向来表示。边缘检测算子检查每个像素的领域并对灰度变化率进行量化,也包括方向的确定,大多数使用基于方向导数掩模求卷积的方法。数字图像处理技术已被广泛应用到生物医学领域，运用计算机对图像进行处理和分析，并进一步完成癌细胞的检测与识别，能有效的协助医生对肿瘤癌症做出诊断。在识别癌细胞时，需要做出定量的结果，人眼很难准确的完成这类工作，而利用计算机图像处