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边缘检测是数字图像处理中的重要内容，它是底层视觉处理中最重要的环节之一也是实现基于边界的图像分割的基础。本文对图像的边缘检测的各种算法和算子做了总结和分析，通过运用MATLAB对常见的边缘检测算子进行仿真。-Edge detection in digital image processing is an important part of it is the underlying visual processing is one of the most important part of the
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自动检测提取彩色图像边界，使用matlab实现，下载文件后，请将文件后缀改为.m。-Automatic detection of color images to extract the boundary, the use of matlab to achieve, download files, change the file extension to. M.
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图像分割,集合了边缘检测法，阈值检测法，边界分割法，区域分割法等，采用VC++编写，-Image segmentation, a collection of edge detection, threshold detection, boundary segmentation, region segmentation method using VC++ writing,
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该文件夹中有六个M文件，说明如下：
boundary_extraction.m : 目标区域边界抽取主函数，其中调用ostu、four_neighbor、eight_neighbor、globe_graph
otsu.m : 类判别分类法（otsu全局二值化算法）
four_neighbor.m : 四邻域法边界抽取
eight_neighbor.m : 八邻域法边界抽取
globe_graph.m : 全局检测法边界抽取
area_peri.m : 目标区域
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Edge detection result should be enhanced using linear method like Median filter to
remove the garbage around the pupil to gain clear pupil to determine perfect centre. Get the
centre of the pupil by counting the number of black pixels (zero value
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Edge detection result should be enhanced using linear method like Median filter to
remove the garbage around the pupil to gain clear pupil to determine perfect centre. Get the
centre of the pupil by counting the number of black pixels (zero value
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Edge detection result should be enhanced using linear method like Median filter to
remove the garbage around the pupil to gain clear pupil to determine perfect centre. Get the
centre of the pupil by counting the number of black pixels (zero value
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Edge detection result should be enhanced using linear method like Median filter to
remove the garbage around the pupil to gain clear pupil to determine perfect centre. Get the
centre of the pupil by counting the number of black pixels (zero value
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Edge detection result should be enhanced using linear method like Median filter to
remove the garbage around the pupil to gain clear pupil to determine perfect centre. Get the
centre of the pupil by counting the number of black pixels (zero value
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小波分析用于图像去噪、边界扭曲、压缩、数字水印、小波包分解与重构、图像融合及边缘检测。-Wavelet analysis is used to image denoising, boundary twisted, compression, digital watermark, and wavelet packet decomposition and reconstruction, image fusion and edge detection.
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Abstract—Three-feature based automatic lane detection al-
gorithm (TFALDA) is a new lane detection algorithm which is
simple, robust, and efficient, thus suitable for real-time processing
in cluttered road environments without a priori knowledg
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输入为一幅边缘检测的二值边界图像，输出为所有边界线的位置，返回存储每条边界线位置的结构数组。-Input binary boundary image of an edge detection, the output of the location of the boundary line, return the array of structures of the storage location of each boundary line.
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VS2008环境下使用C++进行图像分割编程
具体功能如下：
边缘检测法
阈值分割法
边界分割法
区域分割法
彩色分割法-VS2008 environment the use C++ image segmentation programming features are as follows: color segmentation method for edge detection threshold segmentation boundary segmentation
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边缘是图像最基本的特征，是图像分割的第一步。经典的边缘检测方法如：Roberts，Sobel，Prewitt，Kirsch，Laplace等方法，基本都是对原始图像中象素的小邻域构造边缘检测算子，进行行一阶微分或二阶微分运算，求得梯度最大值或二阶导数的过零点，最后选取适当的阀值提取边界。由于这些算法涉及梯度的运算，因此均存在对噪声敏感、计算量大等缺点。在实践中，发现SUSAN算法只基于对周边象素的灰
-Edge of the basic characteristics of the imag
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此程序是运用VC对图像进行二值化处理，然后边缘检测从而识别出图像边界-This procedure is to use VC binarizing processing on the image, and then edge detection in order to identify the image boundary
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本文针对()*+, 模型应用于图像边缘检测时对于噪音过于敏感的不足和易于从弱边界溢出!提出了一种
新的约束力" 在新的约束力的作用下!()*+, 模型可以很好地减小噪音的干扰!防止曲线溢出弱边界!并使初始曲
线具有更大的选择空间!使模型的分割性能更好" 实验证明该模型可以分割出较好的目标边界"-In this paper, ()*+ model is applied to the image edge detection too sensitive to the lack of nois
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链码直线检测，采用求取图像边界，然后边界跟踪，判别直线-Chain code line detection, to strike the image boundary, then the boundary tracking, discriminant linear
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Object Tracking using Egg shape boundary model with failure detection and recovery methods using HSV color space
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图像分割采用了边缘检测，边界跟踪、区域生长、区域分离和聚合等方法-Image segmentation using edge detection, boundary tracking, region growing, region separation and polymerization method
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boundary shot detection
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