Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Handbook of pattern recognition & computer vision
Modern Control Systems
Rapid extraction of image texture by co-occurrence using a hybrid data structure
Computers & Geosciences
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Texture Analysis Using Gaussian Weighted Grey Level Co-Occurrence Probabilities
CRV '04 Proceedings of the 1st Canadian Conference on Computer and Robot Vision
Design-based texture feature fusion using Gabor filters and co-occurrence probabilities
IEEE Transactions on Image Processing
3D computation of gray level co-occurrence in hyperspectral image cubes
EMMCVPR'07 Proceedings of the 6th international conference on Energy minimization methods in computer vision and pattern recognition
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Texture analysis has been used extensively in the computer-assisted interpretation of digital imagery. A popular texture feature extraction approach is the grey level co-occurrence probability (GLCP) method. Most investigations consider the use of the GLCP texture features for classification purposes only, and do not address segmentation performance. Specifically, for segmentation, the pixels in an image located near texture boundaries have a tendency to be misclassified. Boundary preservation when using the GLCP texture features for image segmentation is important. An advancement which exploits spatial relationships has been implemented. The generated features are referred to as weighted GLCP (WGLCP) texture features. In addition, an investigation for selecting suitable GLCP parameters for improved boundary preservation is presented. From the tests, WGLCP features provide improved boundary preservation and segmentation accuracy at a computational cost. As well, the GLCP correlation statistical parameter should not be used when segmenting images with high contrast texture boundaries.