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Review of wavelet-based unsupervised texture segmentation, advantage of adaptive wavelets

机译:基于小波的无监督纹理分割研究,自适应小波的优势

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Wavelet-based segmentation approaches are widely used for texture segmentation purposes because of their ability to characterise different textures. In this study, the authors assess the influence of the chosen wavelet and propose to use the recently introduced empirical wavelets. We show that the adaptability of the empirical wavelet permits to reach better results than classic wavelets. To focus only on the textural information, they also propose to perform a cartoon + texture decomposition step before applying the segmentation algorithm. The proposed method is tested on six classic benchmarks, based on several popular texture images.
机译:基于小波的分割方法由于能够表征不同纹理而被广泛用于纹理分割。在这项研究中,作者评估了所选小波的影响,并建议使用最近引入的经验小波。我们表明,经验小波的适应性比经典小波可以达到更好的结果。为了仅关注纹理信息,他们还建议在应用分割算法之前执行卡通+纹理分解步骤。基于几种流行的纹理图像,该方法在六个经典基准上进行了测试。

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