Optimization Methods for Image Thresholding: A review

Abd, Bushra A.; Hussein Alrawi, Azmi T.; Bassel, Atheer

doi:10.37652/juaps.2023.178875

	Optimization Methods for Image Thresholding: A review
Journal of University of Anbar for Pure Science
Volume 17, Issue 1, June 2023, Pages 137-148 PDF (634.44 K)
Document Type: Review Paper
DOI: 10.37652/juaps.2023.178875
Authors
Bushra A. Abd^* ¹; Azmi T. Hussein Alrawi²; Atheer Bassel³
¹Computer Science,Computer science and information technology,Anbar University,Ramadi,Iraq
²University of Anbar College of Computer Science and Information Technology Department of Computer Science
³University of Anbar College of Computer Science and Information Technology Department of Computer Science
Abstract
Setting a border with the proper gray level in processing images to separate objects from their backgrounds is crucial. One of the simplest and most popular methods of segmenting pictures is histogram-based thresholding. Thresholding is a common technique for image segmentation because of its simplicity. Thresholding is used to separate the Background of the image from the Foreground. There are many methods of thresholding. This paper aims to review many previous studies and mention the types of thresholding. It includes two types: the global and local thresholding methods and each type include a group of methods. The global thresholding method includes (the Otsu method, Kapur's entropy method, Tsallis entropy method, Hysteresis method, and Fuzzy entropy method), and the local thresholding method includes ( Ni-Black method and Bernsen method). The optimization algorithms(Genetic Algorithm, Particle Swarm Optimization, Bat Algorithm, Modified Grasshopper Optimization, Firefly Algorithm, Cuckoo Search, Tabu Search Algorithm, Simulated Annealing, and Jaya Algorithm) used along with thresholding methods are also illustrated.
Keywords
Method of Thresholding; The optimization algorithms; Simulated Annealing; Jaya
Supplementary Files 14 Comp.docx 14 Comp.Doi.pdf 14 Comp.Doi.pdf
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