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Image segmentation based on the hybrid total variation model and the K-means clustering strategy
2016
Inverse Problems and Imaging
The performance of image segmentation highly relies on the original inputting image. When the image is contaminated by some noises or blurs, we can not obtain the efficient segmentation result by using direct segmentation methods. In order to efficiently segment the contaminated image, this paper proposes a two step method based on the hybrid total variation model with a box constraint and the K-means clustering method. In the first step, the hybrid model is based on the weighted convex
doi:10.3934/ipi.2016022
fatcat:2sbmgisvgjcyrndpinhmnot7im