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A Two-Stage Image Segmentation Model for Multi-Channel Images

Part of: General convexity

Published online by Cambridge University Press:  12 April 2016

Zhi Li  and
Tieyong Zeng
Zhi Li
Affiliation:
Department of Mathematics, Hong Kong Baptist University, Hong Kong
Tieyong Zeng*
Affiliation:
Department of Mathematics, Hong Kong Baptist University, Hong Kong
*
*Corresponding author. Email addresses:zhi_li@life.hkbu.edu.hk (Z. Li), zeng@hkbu.edu.hk (T. Zeng)
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Abstract

This paper introduces a two-stage model for multi-channel image segmentation, which is motivated by minimal surface theory. Indeed, in the first stage, we acquire a smooth solution u from a convex variational model related to minimal surface property and different data fidelity terms are considered. This minimization problem is solved efficiently by the classical primal-dual approach. In the second stage, we adopt thresholding to segment the smoothed image u. Here, instead of using K-means to determine the thresholds, we propose a more stable hill-climbing procedure to locate the peaks on the 3D histogram of u as thresholds, in the meantime, this algorithm can also detect the number of segments. Finally, numerical results demonstrate that the proposed method is very robust against noise and superior to other image segmentation approaches.

Keywords

Image segmentationminimal surfacemulti-channelprimal-dual methodtotal variation

MSC classification

Secondary: 52A41: Convex functions and convex programs 65K10: Optimization and variational techniques 90C25: Convex programming 90C47: Minimax problems
Type
Research Article
Information
Communications in Computational Physics , Volume 19 , Issue 4 , April 2016 , pp. 904 - 926
Copyright
Copyright © Global-Science Press 2016 

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