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A study on Robust Feature Image for Texture Classification and Detection  

Kim, Young-Sub (동아대학교 전자공학과)
Ahn, Jong-Young (한국폴리텍2대 컴퓨터정보과)
Kim, Sang-Bum (한국폴리텍여자대학 디지털정보과)
Hur, Kang-In (동아대학교 전자공학과)
Publication Information
The Journal of the Institute of Internet, Broadcasting and Communication / v.10, no.5, 2010 , pp. 133-138 More about this Journal
Abstract
In this paper, we make up a feature image including spatial properties and statistical properties on image, and format covariance matrices using region variance magnitudes. By using it to texture classification, this paper puts a proposal for tough texture classification way to illumination, noise and rotation. Also we offer a way to minimalize performance time of texture classification using integral image expressing middle image for fast calculation of region sum. To estimate performance evaluation of proposed way, this paper use a Brodatz texture image, and so conduct a noise addition and histogram specification and create rotation image. And then we conduct an experiment and get better performance over 96%.
Keywords
Texture classification; Feature image; Covariance matrix;
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  • Reference
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