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EmGMM

于 2016-03-26 发布文件大小:5KB

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下载积分: 1 下载次数: 8

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高斯混合模型的最大期望迭代求解算法,可用于图像区域灰度分布估计(Expectation maximazation(EM) for Gaussian mixture model(GMM) )

文件列表：

EmGm
....\logsumexp.m,487,2016-03-02
....\mixGaussEm.m,2256,2016-03-07
....\mixGaussEm_demo.m,317,2016-03-07
....\mixGaussPred.m,952,2016-03-07
....\mixGaussRnd.m,2237,2016-03-07
....\plotClass.m,920,2015-12-27
license.txt,1307,2016-03-13

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Gray Level Edge Thinning Method Abstract. An effective edge thinning algorithm is important in image segmentation and object identification since it increases the possibility of success in detecting objects in the image and saves the processing time in the further steps such as labeling and image transformation. We investigate some gray level edge thinning method that is based on the gradient magnitude within 3x3 local neighborhood. The new method has been tested with various types of images the real world. Experimental results show that this method produces more accurate images than those of the popular non-maximum suppression methods do.

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EmGMM

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