TEXTURE CLASSIFICATION AND SEGMENTATION USING WAVELET FRAMES

被引:964
作者
UNSER, M
机构
[1] Biomedical Engineering and Instrumentation Program, National Center for Research Resources, National Institutes of Health, Bethesda
关键词
D O I
10.1109/83.469936
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper describes a new approach to the characterization of texture properties at multiple scales using the wavelet transform, The analysis uses an overcomplete wavelet decomposition, which yields a description that is translation invariant. It is shown that this representation constitutes a tight frame of l(2) and that it has a fast iterative algorithm, A texture is characterized by a set of channel variances estimated at the output of the corresponding filter bank, Classification experiments with 12 Brodatz textures indicate that the discrete wavelet frame (DWF) approach is superior to a standard (critically sampled) wavelet transform feature extraction, These results also suggest that this approach should perform better than most traditional single resolution techniques (co-occurrences, local linear transform, and the like). A detailed comparison of the classification performance of various orthogonal and biorthogonal wavelet transforms is also provided. Finally, the DWF feature extraction technique is incorporated into a simple multicomponent texture segmentation algorithm, and some illustrative examples are presented.
引用
收藏
页码:1549 / 1560
页数:12
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