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

In: Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging

Author

Listed:
  • Xiaohao Cai

    (University of Southampton, School of Electronics and Computer Science)

  • Raymond Chan

    (City University of Hong Kong, Department of Mathematics, College of Science)

  • Tieyong Zeng

    (The Chinese University of Hong Kong, Department of Mathematics)

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.

Suggested Citation

  • Xiaohao Cai & Raymond Chan & Tieyong Zeng, 2023. "An Overview of SaT Segmentation Methodology and Its Applications in Image Processing," Springer Books, in: Ke Chen & Carola-Bibiane Schönlieb & Xue-Cheng Tai & Laurent Younes (ed.), Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, chapter 40, pages 1385-1411, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-98661-2_75
    DOI: 10.1007/978-3-030-98661-2_75
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