Fast global minimization of the Chan-Vese model for image segmentation problem
type: Item
instance of: scholarly article
title: Fast global minimization of the Chan-Vese model for image segmentation problem
zbMATH Open document ID: 1486.94013
DOI: 10.1155/2021/2852399
author: Ran Gao
author: Li-Zhen Guo
published in: Discrete Dynamics in Nature and Society
publication date: 2022-01-07T00:00:00Z
review text: Summary: The segmentation of weak boundary is still a difficult problem, especially sensitive to noise, which leads to the failure of segmentation. Based on the previous works, by adding the boundary indicator function with \(L_{2,1}\) norm, a new convergent variational model is proposed. A novel strategy for the weak boundary image is presented. The existence of the minimizer for our model is given, by using the alternating direction method of multipliers (ADMMs) to solve the model. The experiments show that our new method is robust in segmentation of objects in a range of images with noise, low contrast, and direction.
Mathematics Subject Classification ID: 94A08
Mathematics Subject Classification ID: 68U10
zbMATH DE Number: 7453811
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2021/2852399
OpenAlex ID: W4200465740
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description: scientific article; zbMATH DE number 7453811