Description: 数字图像的边缘检测
本科毕业设计（边缘检测是数字图像处理中的重要内容。本文首先对图像的边缘检测的各种算法和算子做了总结和分析。Canny最早提出了边缘检测的三条连续准则:最优检测结果、最优定位和低重复响应,并在这些准则的基础上得到了“最优线性滤波器”―高斯函数的一阶导数。经过十几年的发展,目前已经有了对这个准则的很多改进,本文也对这个方面的工作做了小结。Demigny在理论分析和实践的基础上给出了边缘检测的离散准则,并且证明在离散准则中Canny提出的第三个准则可以被阀值操作所取代。本文利用了数值方法求出了Demigny离散准则下阶梯形边缘检测的最优线性滤波器和对应着它的平滑算子。利用这个算子和Canny边缘检测方法得到了一个完整的边缘检测算法并用VC++实现了这种算法.从算法对大量图像边缘检测的结果来看,这种算法虽然简单但是效果很好,是边缘检测的一种很好的实用方法。-Edge detection is important in image procession. This paper made a summary and analysis of edge detecting algorithm and edge detector. Canny has proposed three continuous criteria to compare the performance of different filters: good detection, good localization and low-responses. Based on these criteria he got optimal filter for edge detection: derivative of Gaussian function. After more than ten years research, Canny’s theory has been ameliorated in many aspects, this paper also made a review of it. Based on the practice and theory. Demigny gave three discrete criteria for edge detection like Canny’s criteria and he has proofed that the third criterion can be replaced by an appropriate thresholding operation. This paper used numerical method to get the optimal filter and smooth operator under the Demigny’s criteria. Then I combine these filters and Canny’s edge detecting technique to get an integrated edge detecting algorithm. I have implemented the algorithm using VC++. From the res Platform: |
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Description: 该演示中包含的代码演示如何氡相似的功能，可以用来提高（以及部分）在Connectome电磁图像单元格边界。
请举出下列文件如果您发现此代码有用：
Ritwik库马尔，阿梅里奥五雷纳和Hanspeter Pfister说“氡样的特点及其应用Connectomics”，接受，IEEE计算机学会研讨会在生物医学图像分析（MMBIA）2010年数学方法
http://seas.harvard.edu/〜 rkkumar
radonLikeFeaturesDemo.m：演示边缘增强用氡相似的功能，
getEdgeFeatures.m：助手为高斯函数的二阶导数滤波器
makeBarFilters.m：助手为高斯函数的二阶导数滤波器
sample.png：示例图像在此演示使用-The demo included in the code demonstrates how Radon-Like Features can be used to enhance (and segment) cell boundaries in Connectome EM images.
Please cite the following paper if you find this code useful:
Ritwik Kumar, Amelio V. Reina & Hanspeter Pfister, “Radon-Like Features and their Application to Connectomics”, accepted, IEEE Computer Society Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA) 2010
http://seas.harvard.edu/~rkkumar
radonLikeFeaturesDemo.m : Demonstrates edge enhancement using Radon-Like Features
getEdgeFeatures.m : Helper function for Gaussian Second Derivative Filters
makeBarFilters.m : Helper function for Gaussian Second Derivative Filters
sample.png : Sample image for use in this demo
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