Rotation-invariant texture classification using circular Gabor wavelets

Qingbo Yin; Jong-Nam Kim; Liran Shen

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Rotation-invariant texture classification using circular Gabor wavelets

机译：Rotation-invariant texture classification using circular Gabor wavelets

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摘要

Rotation-invariant texture classification is one of the most challenging problems in computer vision. We present a new and effective method for rotation-invariant texture classification based on circular Gabor wavelets. Two group features can be constructed by the mean and variance of the circular Gabor filtered images and rotation invariants. Using the two group features, a discriminant can be found to classify rotated images. The proposed method is evaluated on three public texture databases: Brodatz, CUReT, and UIUCTex. The experimental results, based on different testing data sets, show that the proposed method has comparatively high correct classification rates not only for the rotated images, but also for the images under different illuminations and viewing directions. The proposed method is robust to additive white noise.

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来源
《Optical Engineering》 |2009年第1期|017001-1-017001-13-0|共13页
作者
Qingbo Yin; Jong-Nam Kim; Liran Shen;
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作者单位

Pukyong National University, Division of Electronic Computer and Telecommunication Engineering, 599-1 Daeyeon-dong Nam-gu, Busan, 608-737 Korea;

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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种英语
中图分类光学仪器;
关键词
texture classification; rotation invariant; texture analysis; circular Gabor wavelets;

Rotation-invariant texture classification using circular Gabor wavelets

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