An Overview of SaT Segmentation Methodology and Its Applications in Image Processing

Xiaohao CAI; Raymond CHAN; Tieyong ZENG

doi:10.1007/978-3-030-98661-2_75

An Overview of SaT Segmentation Methodology and Its Applications in Image Processing

Xiaohao CAI^*, Raymond CHAN^*, Tieyong ZENG^*

^*Corresponding author for this work

Research output: Book Chapters | Papers in Conference Proceedings › Book Chapter › Research › peer-review

1 Citation (Scopus)

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.

Original language	English
Title of host publication	Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging: Mathematical Imaging and Vision
Editors	Ke CHEN, Carola-Bibiane SCHÖNLIEB, Xue-Cheng TAI, Laurent YOUNES
Publisher	Springer, Cham
Pages	1385-1411
Number of pages	27
ISBN (Electronic)	9783030986612
ISBN (Print)	9783030986605
DOIs	https://doi.org/10.1007/978-3-030-98661-2_75
Publication status	Published - 2023
Externally published	Yes

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2023.

Keywords

Image processing
Image segmentation
Inverse problem
Variational model

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10.1007/978-3-030-98661-2_75

Cite this

CAI, X., CHAN, R., & ZENG, T. (2023). An Overview of SaT Segmentation Methodology and Its Applications in Image Processing. In K. CHEN, C.-B. SCHÖNLIEB, X.-C. TAI, & L. YOUNES (Eds.), Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging: Mathematical Imaging and Vision (pp. 1385-1411). Springer, Cham. https://doi.org/10.1007/978-3-030-98661-2_75

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keywords = "Image processing, Image segmentation, Inverse problem, Variational model",

author = "Xiaohao CAI and Raymond CHAN and Tieyong ZENG",

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year = "2023",

doi = "10.1007/978-3-030-98661-2_75",

language = "English",

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An Overview of SaT Segmentation Methodology and Its Applications in Image Processing. / CAI, Xiaohao; CHAN, Raymond; ZENG, Tieyong.
Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging: Mathematical Imaging and Vision. ed. / Ke CHEN; Carola-Bibiane SCHÖNLIEB; Xue-Cheng TAI; Laurent YOUNES. Springer, Cham, 2023. p. 1385-1411.

Research output: Book Chapters | Papers in Conference Proceedings › Book Chapter › Research › peer-review

TY - CHAP

T1 - An Overview of SaT Segmentation Methodology and Its Applications in Image Processing

AU - CAI, Xiaohao

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AU - ZENG, Tieyong

N1 - Publisher Copyright: © Springer Nature Switzerland AG 2023.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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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KW - Image segmentation

KW - Inverse problem

KW - Variational model

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EP - 1411

BT - Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging: Mathematical Imaging and Vision

A2 - CHEN, Ke

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A2 - TAI, Xue-Cheng

A2 - YOUNES, Laurent

PB - Springer, Cham

ER -

An Overview of SaT Segmentation Methodology and Its Applications in Image Processing

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Keywords

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