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A Robust Graph Theoretic Approach for Image Segmentation

机译:图像分割的鲁棒图理论方法

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摘要

This paper presents a new robust graph theoretic approach for image segmentation. The proposed method which is capable of accurately locating region boundaries has the following salient features. First, it is a non-supervised approach which reflects the non-local properties of the image. Second, it guarantees that the regions are connected. Finally, it produces robust results which is almost unaffected by the influences of outliers. In thistechnique, at each step, a minimum weight edge is selected and the two regions connected by the minimum weight edge are considered for merge. The merging of regions is carried out, if the mean of the edges connecting the two regions is smaller than the maximum of the mean of the intra region edges along with the threshold value.
机译:本文提出了一种新的鲁棒图理论的图像分割方法。所提出的能够精确定位区域边界的方法具有以下显着特征。首先,这是一种非监督方法,可反映图像的非局部属性。其次,它保证了区域之间的连接。最后,它产生的鲁棒结果几乎不受异常值的影响。在该技术中,在每个步骤中,选择最小权重边缘,并考虑通过最小权重边缘连接的两个区域的合并。如果连接两个区域的边缘的平均值小于区域内边缘的平均值与阈值的最大值,则执行区域合并。

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