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GLCM(Gray-level Co-occurrence Matrix) Implementation
This project forked from mck0517/glcm
GLCM(Gray-level Co-occurrence Matrix) Implementation
* GLCM(Gray-level Co-occurrence Matrix) Implementation using C language - Implementation of GLCM features(http://haralick.org/journals/TexturalFeatures.pdf) - 13 features Implementation in the haralick`s features. - gray-level quantization and some code optimization(ex: loop calculation) for fast processing speed. - No 3rd party lib dependency, OpenCV was used only for image I/O handling. * File Description - Param.h: parameters setting for quantization level, test image size, neighborhood pixel distance, angle range - Vision_Test: main fuction - GLCM: image quantization, GLCM calculation - Data.h: arrays for algorithms * Program Description - You can use any kind of OpenCV Version. - For the minimization of dynamic memory allocation, I use array buffer. Thus, You must set the width and height(IMAGE_WIDTH, IMAGE_HEIGHT in Param.h) of the image in advance. - OpenCV was used only for image reading fuction(ex: cvLoadImage("lenna.bmp", CV_LOAD_IMAGE_GRAYSCALE)). - This program use only gray scale image format But you can use color image format using above opencv code. - If you run the main fuction(Vision_Test.cpp), you can see the results of the haralick`s features. - haralick`s features: Energy, Contrast, Correlation, Sum of Squares, Local Stability, Sum Average, Sum Variance, Sum Entropy, Entropy, Difference Variance, Difference Entropy, Information Measure Correlation 1 and Information Measure Correlation 2.
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