The iterative convolution-thresholding method (ICTM) for image segmentation

Dong Wang University of Utah Xiao-Ping Wang The Hong Kong University of Science and Technology

Numerical Analysis and Scientific Computing mathscidoc:1908.25002

In this paper, we propose a novel iterative convolution-thresholding method (ICTM) that is applicable to a range of variational models for image segmentation. A variational model usually minimizes an energy functional consisting of a fidelity term and a regularization term. In the ICTM, the interface between two different segment domains is implicitly represented by their characteristic functions. The fidelity term is usually written as a linear functional of the characteristic functions and the regularized term is approximated by a functional of characteristic functions in terms of heat kernel convolution. This allows us to design an iterative convolution-thresholding method to minimize the approximate energy. The method is simple, efficient and enjoys the energy-decaying property. Numerical experiments show that the method is easy to implement, robust and applicable to various image segmentation models.
Convolution, thresholding, image segmentation, heat kernel
[ Download ] [ 2019-08-20 10:31:39 uploaded by dwang ] [ 840 downloads ] [ 0 comments ]
  title={The iterative convolution-thresholding method (ICTM) for image segmentation},
  author={Dong Wang, and Xiao-Ping Wang},
Dong Wang, and Xiao-Ping Wang. The iterative convolution-thresholding method (ICTM) for image segmentation.
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