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Texture analysis and classification with tree-structured wavelet transform

机译:树状小波变换的纹理分析与分类

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摘要

A multiresolution approach based on a modified wavelet transform called the tree-structured wavelet transform or wavelet packets is proposed. The development of this transform is motivated by the observation that a large class of natural textures can be modeled as quasi-periodic signals whose dominant frequencies are located in the middle frequency channels. With the transform, it is possible to zoom into any desired frequency channels for further decomposition. In contrast, the conventional pyramid-structured wavelet transform performs further decomposition in low-frequency channels. A progressive texture classification algorithm which is not only computationally attractive but also has excellent performance is developed. The performance of the present method is compared with that of several other methods.
机译:提出了一种基于改进的小波变换的多分辨率方法,称为树型小波变换或小波包。这种转换的发展是受到以下观察的激励:可以将一大类自然纹理建模为准周期信号,其主导频率位于中频通道中。通过该变换,可以放大任何所需的频道以进行进一步分解。相反,常规的金字塔结构小波变换在低频信道中执行进一步的分解。开发了一种渐进的纹理分类算法,该算法不仅在计算上吸引人,而且具有出色的性能。将本方法的性能与其他几种方法的性能进行比较。

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