An overview of SaT segmentation methodology and its applications in image processing
An overview of SaT segmentation methodology and its applications in image processing
As a fundamental and challenging task in many subjects such as image processing and computer vision, image segmentation is of great importance but is constantly challenging to deliver, particularly, when the given images or data are corrupted by different types of degradations like noise, information loss, and/or blur. In this article, we introduce a segmentation methodology – smoothing and thresholding (SaT) – which can provide a flexible way of producing superior segmentation results with fast and reliable numerical implementations. A bunch of methods based on this methodology are to be presented, including many applications with different types of degraded images in image processing.
Image processing, Image segmentation, Inverse problem, Mumford-shah model, Variational model
1385-1411
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Chan, Raymond
9185af9b-f073-4e3d-8f22-1dac8d28db58
Zeng, Tieyong
8bae04dd-2c0d-49f2-898b-30cdc0f5e286
Schönlieb, Carola-Bibiane
25 February 2023
Cai, Xiaohao
de483445-45e9-4b21-a4e8-b0427fc72cee
Chan, Raymond
9185af9b-f073-4e3d-8f22-1dac8d28db58
Zeng, Tieyong
8bae04dd-2c0d-49f2-898b-30cdc0f5e286
Schönlieb, Carola-Bibiane
Cai, Xiaohao, Chan, Raymond and Zeng, Tieyong
(2023)
An overview of SaT segmentation methodology and its applications in image processing.
In,
Chen, Ke, Schönlieb, Carola-Bibiane, Tai, Xue-Cheng and Younes, Laurent
(eds.)
Handbook of mathematical models and algorithms in computer vision and imaging: mathematical imaging and vision.
Springer Cham, .
(doi:10.1007/978-3-030-98661-2_75).
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Book Section
Abstract
As a fundamental and challenging task in many subjects such as image processing and computer vision, image segmentation is of great importance but is constantly challenging to deliver, particularly, when the given images or data are corrupted by different types of degradations like noise, information loss, and/or blur. In this article, we introduce a segmentation methodology – smoothing and thresholding (SaT) – which can provide a flexible way of producing superior segmentation results with fast and reliable numerical implementations. A bunch of methods based on this methodology are to be presented, including many applications with different types of degraded images in image processing.
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Published date: 25 February 2023
Additional Information:
This article is published in the living reference work version of the Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging.
Keywords:
Image processing, Image segmentation, Inverse problem, Mumford-shah model, Variational model
Identifiers
Local EPrints ID: 451828
URI: http://eprints.soton.ac.uk/id/eprint/451828
PURE UUID: d963601a-0ac2-451a-87ce-68e16397b121
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Date deposited: 29 Oct 2021 16:30
Last modified: 17 Mar 2024 04:01
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Contributors
Author:
Xiaohao Cai
Author:
Raymond Chan
Author:
Tieyong Zeng
Editor:
Ke Chen
Editor:
Carola-Bibiane Schönlieb
Editor:
Xue-Cheng Tai
Editor:
Laurent Younes
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