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Infrared Ship Target Segmentation Based on Spatial Information Improved FCM

机译:基于空间信息改进FCM的红外舰船目标分割

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摘要

Segmentation of infrared (IR) ship images is always a challenging task, because of the intensity inhomogeneity and noise. The fuzzy C-means (FCM) clustering is a classical method widely used in image segmentation. However, it has some shortcomings, like not considering the spatial information or being sensitive to noise. In this paper, an improved FCM method based on the spatial information is proposed for IR ship target segmentation. The improvements include two parts: 1) adding the nonlocal spatial information based on the ship target and 2) using the spatial shape information of the contour of the ship target to refine the local spatial constraint by Markov random field. In addition, the results of K -means are used to initialize the improved FCM method. Experimental results show that the improved method is effective and performs better than the existing methods, including the existing FCM methods, for segmentation of the IR ship images.
机译:由于强度不均匀和噪声,分割红外(IR)船舶图像始终是一项艰巨的任务。模糊C均值(FCM)聚类是一种广泛用于图像分割的经典方法。但是,它有一些缺点,例如不考虑空间信息或对噪声敏感。提出了一种基于空间信息的改进FCM方法进行红外舰船目标分割。改进包括两个部分:1)基于舰船目标添加非局部空间信息; 2)使用舰船目标轮廓的空间形状信息通过马尔可夫随机场细化局部空间约束。另外,将K均值的结果用于初始化改进的FCM方法。实验结果表明,改进后的方法对红外舰船图像的分割是有效的,并且比包括FCM方法在内的现有方法具有更好的分割效果。

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