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Geostatistical Entropy for Texture Analysis: An Indicator Kriging Approach
Author(s) -
Pham Tuan D.
Publication year - 2014
Publication title -
international journal of intelligent systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.291
H-Index - 87
eISSN - 1098-111X
pISSN - 0884-8173
DOI - 10.1002/int.21639
Subject(s) - computer science , artificial intelligence , entropy (arrow of time) , image texture , texture (cosmology) , texture synthesis , image (mathematics) , pattern recognition (psychology) , image processing , perception , computer vision , physics , quantum mechanics , neuroscience , biology
Texture analysis is a major research topic in intelligent image processing. Its useful applications, including object detection and classification, to various fields of engineering, science, medicine, biology, and creative arts have been increasingly reported. Texture is a fundamental aspect of human vision and perception by which different types of objects can be distinguished through their appearance, ranging from distinctive to subtle roughness. Given tremendous efforts in terms of both theoretical developments and applications, texture analysis still remains a challenging area of research in image analysis and pattern recognition. This paper presents a novel and practical image texture analysis method using the fundamentals of geostatistics and the concept of entropy in information theory. Experimental results on medical and document image data have shown the superior performance of the proposed approach over its related texture analysis methods.