Rotation and gray-scale transform-invariant texture classification using spiral resampling, subband decomposition, and hidden Markov model

Wen-Rong Wu*, Shieh Chung Wei

*此作品的通信作者

研究成果: Article同行評審

98 引文 斯高帕斯(Scopus)

摘要

This paper proposes a new texture classification algorithm that is invariant to rotation and gray-scale transformation. First, we convert two-dimensional (2-D) texture images to one-dimensional (1-D) signals by spiral resampling. Then, we use a quadrature mirror filter (QMF) bank to decompose sampled signals into subbands. In each band, we take high-order autocorrelation functions as features. Features in different bands, which form a vector sequence, are then modeled as a hidden Markov model (HMM). During classification, the unknown texture is matched against all the models and the best match is taken as the classification result. Simulations showed that the highest correct classification rate for 16 kinds of texture was 95.14%.

原文English
文章編號536891
頁(從 - 到)1423-1434
頁數12
期刊IEEE Transactions on Image Processing
5
發行號10
DOIs
出版狀態Published - 10月 1996

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