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An Accurate and Practical Explicit Hybrid Method for the Chan-Vese Image Segmentation Model

Authors
Jeong, DaraeKim, SangkwonLee, ChaeyoungKim, Junseok
Issue Date
7월-2020
Publisher
MDPI
Keywords
image processing; Allen-Cahn equation; finite difference method
Citation
MATHEMATICS, v.8, no.7
Indexed
SCIE
SCOPUS
Journal Title
MATHEMATICS
Volume
8
Number
7
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/54951
DOI
10.3390/math8071173
ISSN
2227-7390
Abstract
In this paper, we propose a computationally fast and accurate explicit hybrid method for image segmentation. By using a gradient flow, the governing equation is derived from a phase-field model to minimize the Chan-Vese functional for image segmentation. The resulting governing equation is the Allen-Cahn equation with a nonlinear fidelity term. We numerically solve the equation by employing an operator splitting method. We use two closed-form solutions and one explicit Euler's method, which has a mild time step constraint. However, the proposed scheme has the merits of simplicity and versatility for arbitrary computational domains. We present computational experiments demonstrating the efficiency of the proposed method on real and synthetic images.
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