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fuzzy_Hough_transform

于 2015-04-11 发布文件大小:373KB

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代码说明：

Fuzzy hough 变换的matlab代码实现，很详细，希望大家试试(Fuzzy hough transformation code)

文件列表：

fuzzy Hough transform
.....................\common
.....................\......\cedge.m,726,2009-02-13
.....................\......\click.m,719,2009-02-24
.....................\......\htcircles.m,4698,2009-02-13
.....................\......\iminvert.m,113,2009-02-24
.....................\......\imnorm.m,297,2009-02-24
.....................\......\ndiff.m,230,2009-02-13
.....................\......\pcircle.m,1652,2009-02-13
.....................\......\pcircles.m,709,2009-02-12
.....................\......\pline.m,2448,2009-03-05
.....................\......\rdirproc.m,1509,2009-07-03
.....................\data
.....................\....\images

.....................\....\......\SA06.bmp,66614,2009-01-13
.....................\....\......\VLA05.bmp,103478,2009-01-23
.....................\....\patterns
.....................\....\........\HLA00_pat.mat,229,2009-11-12
.....................\....\........\HL_heart.mat,368,2009-02-21
.....................\....\........\HL_rlung.mat,235,2009-02-21
.....................\demos
.....................\.....\gfht_demo1.m,1027,2010-10-08
.....................\.....\gfht_demo2.m,2786,2010-10-08
.....................\gfht.m,4614,2010-10-07
license.txt,1309,2014-02-12

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％ EM algorithm for k multidimensional Gaussian mixture estimation ％％ Inputs: ％ X(n,d) - input data, n=number of observations, d=dimension of variable ％ k - maximum number of Gaussian components allowed ％ ltol - percentage of the log likelihood difference between 2 iterations ([] for none) ％ maxiter - maximum number of iteration allowed ([] for none) ％ pflag - 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) ％ Init - structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) ％％ Ouputs: ％ W(1,k) - estimated weights of GM ％ M(d,k) - estimated mean vectors of GM ％ V(d,d,k) - estimated covariance matrices of GM ％ L - log likelihood of estimates ％( EM algorithm for k multidimensional Gaussian mixture estimation Inputs: X (n, d)- input data, n = number of observations, d = dimension of variable k- maximum number of Gaussian components allowed ltol- percentage of the log likelihood difference between 2 iterations ([] for none) maxiter- maximum number of iteration allowed ([] for none) pflag- 1 for plotting GM for 1D or 2D cases only, 0 otherwise ([] for none) Init- structure of initial W, M, V: Init.W, Init.M, Init.V ([] for none) Ouputs: W (1, k)- estimated weights of GM M (d, k)- estimated mean vectors of GM V (d, d, k)- estimated covariance matrices of GM L- log likelihood of estimates)

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