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Published in:
2015 | OriginalPaper | Chapter
Rotation Invariant Texture Analysis Based on Co-occurrence Matrix and Tsallis Distribution
Authors : Mateus Habermann, Felipe Berla Campos, Elcio Hideiti Shiguemori
Published in: Advances in Swarm and Computational Intelligence
Publisher: Springer International Publishing
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This article addressed some extensions of a texture classifier invariant to rotations. Originally, that classifier is an improvement of the seminal Haralick’s paper in a sense that the former is rotation invariant due to a circular kernel, which encompasses two concentric circles with different radii and then the co-occurrence matrix is formed. It is not considered only pixels falling exactly on the circle, but also others in its vicinity according to a Gaussian scattering. Firstly, 6 attributes are computed from each of the 18 texture patterns, after that texture patterns are rotated and a correct classification, considering Euclidian distance, is sought. The present paper assesses the performance of the afore-mentioned approach with some alterations: Tsallis rather than Gaussian distribution; addition of noise to rotated images before classification; and Principal Components Analysis during the extraction of features.