EPFL | Biomedical Imaging Group | Texture Discrimination

Texture Discrimination Using Wavelets

M. Unser

Proceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'93), New York NY, USA, June 15-17, 1993, pp. 640–641.

A new approach to the characterization of texture properties at multiple scales using an overcomplete wavelet transform is described. It is shown that this representation constitutes a tight frame of l₂, 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. This result also suggests that this approach should perform better than most traditional single resolution techniques (co-occurrences, local linear transform, etc…). A detailed comparison of the classification performance of various orthogonal and biorthogonal wavelet transforms is provided. The DWF feature extraction technique is incorporated into a simple multiple-component texture segmentation algorithm. Some examples are presented.

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	Conference on Computer Vision and Pattern Recognition ({CVPR'93})",
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address="New York NY, USA",
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