Automatic prior shape selection for image edge detection with modified Mumford–Shah model

Yuying Shi; Zhimei Huo; Jing Qin; Yilin Li

doi:10.1016/j.camwa.2019.09.021

Automatic prior shape selection for image edge detection with modified Mumford–Shah model

Yuying Shi, Zhimei Huo, Jing Qin, Yilin Li

Mathematics

Research output: Contribution to journal › Article › peer-review

10 Scopus citations

Abstract

Edge detection plays an important role in the field of image processing. In this paper, we propose a novel variational model to automatically and adaptively detect one or more prior shapes from the given dictionary to guide the edge detection process. In that way, we can effectively detect the shapes of interest from the test image. Moreover, an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) is proposed to solve this model with guaranteed convergence. A variety of numerical experiments show that the proposed method has achieved ideal performance for edge detection in images with missing information, various types of noise and complicated background, and even multiple objects.

Original language	English
Pages (from-to)	1644-1660
Number of pages	17
Journal	Computers and Mathematics with Applications
Volume	79
Issue number	6
DOIs	https://doi.org/10.1016/j.camwa.2019.09.021
State	Published - Mar 15 2020

Bibliographical note

Publisher Copyright:
© 2019 Elsevier Ltd

Funding

The research is partially supported by NSFC, PR China (No. 11271126 ). The research of Jing Qin is supported by the National Science Foundation, USA grant DMS-1941197 .

Funders	Funder number
National Science Foundation (NSF)	1941197, DMS-1941197
National Natural Science Foundation of China (NSFC)	11271126

Keywords

ADMM
Automatic shape selection
Edge detection
Fixed-point iterative algorithm
Modified Mumford–Shah model
Prior shape

ASJC Scopus subject areas

Modeling and Simulation
Computational Theory and Mathematics
Computational Mathematics

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10.1016/j.camwa.2019.09.021

Cite this

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title = "Automatic prior shape selection for image edge detection with modified Mumford–Shah model",

abstract = "Edge detection plays an important role in the field of image processing. In this paper, we propose a novel variational model to automatically and adaptively detect one or more prior shapes from the given dictionary to guide the edge detection process. In that way, we can effectively detect the shapes of interest from the test image. Moreover, an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) is proposed to solve this model with guaranteed convergence. A variety of numerical experiments show that the proposed method has achieved ideal performance for edge detection in images with missing information, various types of noise and complicated background, and even multiple objects.",

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AU - Huo, Zhimei

AU - Qin, Jing

AU - Li, Yilin

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AB - Edge detection plays an important role in the field of image processing. In this paper, we propose a novel variational model to automatically and adaptively detect one or more prior shapes from the given dictionary to guide the edge detection process. In that way, we can effectively detect the shapes of interest from the test image. Moreover, an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) is proposed to solve this model with guaranteed convergence. A variety of numerical experiments show that the proposed method has achieved ideal performance for edge detection in images with missing information, various types of noise and complicated background, and even multiple objects.

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KW - Prior shape

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Automatic prior shape selection for image edge detection with modified Mumford–Shah model

Abstract

Bibliographical note

Funding

Keywords

ASJC Scopus subject areas

Access to Document

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