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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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2-band discrete wavelet transform (DWT) Dual-Tree Complex Wavelet Packet(The 2-band discrete wavelet transform (DWT) provides an octave-band analysis in the frequency domain, but this might not be ‘optimal’ for a given signal. The discrete wavelet packet transform (DWPT) provides a dictionary of bases over which one can search for an optimal representation (without constraining the analysis to an octave-band one) for the signal at hand. However, it is well known that both the DWT and the DWPT are shift-varying. Also, when these transforms are extended to 2-D and higher dimensions using tensor products, they do not provide a geometrically oriented analysis. The dual-tree complex wavelet transform (DT-CWT), introduced by Kingsbury, is approximately shift-invariant and provides directional analysis in 2-D and higher dimensions. In this paper, we propose a method to implement a dual-tree complex wavelet packet transform (DTCWPT), extending the DT-CWT as the DWPT extends the DWT. To find the best complex wavelet packet frame for a given signal, w)

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EmGMM

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