(1) calculate the LBP pattern of each pixel in the image (equivalent mode, or rotation invariant + equivalent mode). (2) then the LBP eigenvalue histogram of each cell is calculated, and then the histogram is normalized (for each cell, for each bin, h[i]/=sum, sum is the number of all the equivalent classes in a pair of images). (3) finally, the statistical histogram of each cell is connected into a feature vector, that is, the LBP texture feature vector of the whole picture. Then, SVM or other machine learning algorithms can be used for classification and recognition.

Filename	Size	Update
getmapping.m	2762	2018-04-20
LBP.m	6068	2018-04-20
lbptest.m	1683	2018-04-20
test1.bmp	66614	2018-04-20
test2.bmp	66614	2018-04-20
test3.bmp	66614	2018-04-20
test4.bmp	66614	2018-04-20

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