国产欧美精品一区二区,中文字幕专区在线亚洲,国产精品美女网站在线观看,艾秋果冻传媒2021精品,在线免费一区二区,久久久久久青草大香综合精品,日韩美aaa特级毛片,欧美成人精品午夜免费影视

Fuzzy C-means (FCM) has widely been applied to computer vision, which emerged as an important tool for segmenting the structure of image data. However, the effectiveness of this technique lies in its inability to preserve edges and suppress noise, often leading to unsatisfactory segmentations. To solve this problem, we derive a modified FCM algorithm by using guided filter. The first key concept of our method is its linear translation-variant filtering process, which exploits edge-preserving smoothing property to preserve the edge structures in segmentation. The second is that this technique improves the robustness to noise by incorporating the spatial information into the objective function, which are obtained by the mean output of guided filtering. Third, mean shift algorithm is used to get initial cluster centers so that the algorithm does guarantee convergence to the global optimum. The main advantages of the proposed method are that it exhibits robustness to edge-preserving and noise and it can enhance the segmentation accuracy. By comparing with other segmentation methods, experimental results on both synthetic and real remote-sensing images suggest that the proposed method behaves well in segmentation performance.