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Application of two fuzzy c-means clustering algorithms in segmenting the sonar image from a small underwater target into multi-regions

机译:两种模糊c均值聚类算法在将声纳图像从小型水下目标分割为多个区域中的应用

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This paper expatiates on two image segmentation methods based on the improved fuzzy c-means (FCM) clustering algorithms. In the first method named as the method based on the two-dimensional histogram, each clustering sample is a two-dimensional vector structured the gray-level value of each pixel and mean value of each pixel neighborhood. In the second method named as the method considering pixel spatial information, clustering objective function, clustering center and membership function are modified by means of pixel spatial information. Both the first method and the second method take into account pixel spatial information by means of different techniques. The application to segmentation of a sonar image of a small underwater target shows that the first method is as effective as the second method.
机译:本文阐述了基于改进的模糊c均值(FCM)聚类算法的两种图像分割方法。在被称为基于二维直方图的方法的第一种方法中,每个聚类样本是构成每个像素的灰度级值和每个像素邻域的平均值的二维向量。在被称为考虑像素空间信息的方法的第二种方法中,通过像素空间信息来修改聚类目标函数,聚类中心和隶属函数。第一种方法和第二种方法都通过不同的技术考虑了像素空间信息。在水下小目标声纳图像分割中的应用表明,第一种方法与第二种方法一样有效。

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