標題: Rotation and gray-scale transform-invariant texture classification using spiral resampling, subband decomposition, and hidden Markov model
作者: Wu, WR
Wei, SC
電信工程研究所
Institute of Communications Engineering
公開日期: 1-十月-1996
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%.
URI: http://dx.doi.org/10.1109/83.536891
http://hdl.handle.net/11536/986
ISSN: 1057-7149
DOI: 10.1109/83.536891
期刊: IEEE TRANSACTIONS ON IMAGE PROCESSING
Volume: 5
Issue: 10
起始頁: 1423
結束頁: 1434
顯示於類別:期刊論文


文件中的檔案:

  1. A1996VL51300004.pdf