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Preserving boundaries for image texture segmentation using grey level co-occurring probabilities

TitlePreserving boundaries for image texture segmentation using grey level co-occurring probabilities
Publication TypeJournal Article
Year of Publication2006
AuthorsJobanputra, R., and D. A. Clausi
JournalPattern Recognition
Volume39
Pagination234 - 235
ISSN0031-3203
KeywordsCo-occurence probabilities, Co-occurrence matrix, Computer Vision, Digital imaging, Remote Sensing, sea ice, Segmentation, synthetic aperture radar, texture
Abstract

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.

URLhttp://www.sciencedirect.com/science/article/pii/S0031320305002992
DOI10.1016/j.patcog.2005.07.010