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Image thresholding has played an important role in image segmentation. In this paper, we present a novel spatially weighted fuzzy c-means (SWFCM) clustering algorithm for image thresholding. The algorithm is formulated by incorporating the spatial ne
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将用双线性插值归一化后的图像用2*2的窗口扫描加权方向的边缘图，用改进的模糊C均值聚类算法将图像聚类成为候选文字区域和非文字区域。-Bilinear interpolation owned by an after image edge map with 2* 2 the window scanned weighted direction, image clustering with improved fuzzy C-means clustering algorithm to become the
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对《Fuzzy c-means clustering with weighted image patch for image segmentation》这篇文章进行了实现，运行速度在一分钟之内，希望对研究模糊c均值及其拓展算法的人有所帮助。运行testWIPFCM.m即可，注意改掉文件路径-On " Fuzzy c-means clustering with weighted image patch for image segmentation" This article wa
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基于区域的空间域图像融合。先对源图像作小波分解，低频分量加权平均，高频分量用模糊C均值聚类算法进行区域分割，对区域进行基于ssim值的融合，最后小波逆变换得到融合图像。-region based spatial domain method.First do the source image wavelet decomposition low frequency components weighted average of the high frequency component using Fu
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Fuzzy c-means (FCM) clustering has been widely used in image segmentation. However, in spite of its computational efficiency and wide-spread prevalence, the FCM algorithm does not take the spatial information of pixels into consideration, and hence m
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