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An effective color texture image segmentation algorithm based on hermite transform

机译:基于Hermite变换的有效色彩纹理图像分割算法

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In this paper, an efficient color texture image segmentation approach is proposed. The proposed approach uses color and texture information independently. The color information is obtained by converting the RGB color space to Luv color space and each color component is considered as a color descriptor. For texture descriptors, Hermite transform is considered. Hermite transform uses the Hermite filters which are formed by the product of Hermite polynomials with Gaussian function. Instead of using all Hermite filters, a filter selection process is adopted to obtain optimal filters. A feature image is constructed based on the magnitude of each filter response. A region smoothing procedure is employed for both the color components and the feature image in order to make the region smoother while preserving the edge information. To this end, weighted least square edge-preserving filtering is used. Comprehensive experiments were conducted to demonstrate the efficiency of the proposed method, using the Berkeley segmentation dataset. (C) 2018 Elsevier B.V. All rights reserved.
机译:在本文中,提出了一种有效的颜色纹理图像分割方法。所提出的方法独立使用颜色和纹理信息。通过将RGB颜色空间转换为LUV颜色空间来获得颜色信息,并且每个颜色分量被认为是颜色描述符。对于纹理描述符,考虑了Hermite变换。 Hermite变换使用Hermite多项式的产品形成的Hermite过滤器,具有高斯功能。采用过滤器选择过程而不是使用所有Hermite滤波器来获得最佳滤波器。根据每个滤波器响应的幅度构造特征图像。用于颜色分量和特征图像的区域平滑过程,以使区域在保留边缘信息的同时使区域更加光滑。为此,使用加权最小方形边缘保留滤波。使用伯克利分段数据集进行综合实验以证明所提出的方法的效率。 (c)2018 Elsevier B.v.保留所有权利。

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