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/sub 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 l/sub 2/ 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.
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机译:本文介绍了一种使用小波变换在多个尺度上表征纹理特性的新方法。该分析使用了不完全的小波分解,从而产生了翻译不变的描述。结果表明,该表示构成了1 / sub 2 /的紧帧,并且具有快速的迭代算法。纹理的特征在于在相应的滤波器组的输出处估计的一组通道变化。具有l / sub 2 / Brodatz纹理的分类实验表明,离散小波帧(DWF)方法优于标准(临界采样)小波变换特征提取。这些结果还表明,该方法应比大多数传统的单分辨率技术(共现,局部线性变换等)表现更好。还提供了各种正交和双正交小波变换的分类性能的详细比较。最后,将DWF特征提取技术结合到简单的多分量纹理分割算法中,并给出了一些说明性示例。
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