using Matlab a PCA (Principal Component Analysis), k_means image segmentation good example.

Packet : 42346image_mva_0.rar filelist
image_mva_0
image_mva_0\ChooseInitialCentres.m
image_mva_0\dcFuzzy.m
image_mva_0\dcKMeans.m
image_mva_0\fastnnls.m
image_mva_0\fourdifft.m
image_mva_0\histogram.fig
image_mva_0\histogram.m
image_mva_0\image_mva_0.asv
image_mva_0\image_mva_0.fig
image_mva_0\image_mva_0.m
image_mva_0\image_mva_0_0.m
image_mva_0\image_mva_0_1.m
image_mva_0\image_mva_11.m
image_mva_0\image_mva_22.m
image_mva_0\im_class_MLE.m
image_mva_0\invder.m
image_mva_0\MaxMax.m
image_mva_0\mcr.m
image_mva_0\MeanMean.m
image_mva_0\MinMin.m
image_mva_0\pca.m
image_mva_0\pca2.m
image_mva_0\pca_mean.asv
image_mva_0\pca_mean.m
image_mva_0\profiles.fig
image_mva_0\profiles.m
image_mva_0\sgolay.m
image_mva_0\sgolayfilt.m
image_mva_0\simplisma.m
image_mva_0\threshold.fig
image_mva_0\threshold.m
image_mva_0\threshold_grayscale_image.m

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