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

Xiaohao Cai (), Raymond Chan () and Tieyong Zeng ()
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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

Chapter 40 in Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging, 2023, pp 1385-1411 from Springer

Abstract: 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.

Keywords: Image segmentation, Image processing, Mumford-shah model, Variational model; Inverse problem (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-3-030-98661-2_75

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