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 ProceedingsBook ChapterResearchpeer-review

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 languageEnglish
Title of host publicationHandbook of Mathematical Models and Algorithms in Computer Vision and Imaging: Mathematical Imaging and Vision
EditorsKe CHEN, Carola-Bibiane SCHÖNLIEB, Xue-Cheng TAI, Laurent YOUNES
PublisherSpringer, Cham
Pages1385-1411
Number of pages27
ISBN (Electronic)9783030986612
ISBN (Print)9783030986605
DOIs
Publication statusPublished - 2023
Externally publishedYes

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2023.

Keywords

  • Image processing
  • Image segmentation
  • Inverse problem
  • Variational model

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