An adaptive multilevel thresholding method with chaotically-enhanced Rao algorithm.

Autor: Olmez Y; Department of Mechatronics Engineering, Faculty of Technology, University of Firat, 23119 Elazig, Turkey., Sengur A; Department of Electrical and Electronics Engineering, Faculty of Technology, University of Firat, 23119 Elazig, Turkey., Koca GO; Department of Mechatronics Engineering, Faculty of Technology, University of Firat, 23119 Elazig, Turkey., Rao RV; Department of Mechanical Engineering, Sardar Vallabhbhai National Institute of Technology, Surat, Gujarat 395007 India.
Jazyk: angličtina
Zdroj: Multimedia tools and applications [Multimed Tools Appl] 2023; Vol. 82 (8), pp. 12351-12377. Date of Electronic Publication: 2022 Sep 09.
DOI: 10.1007/s11042-022-13671-9
Abstrakt: Multilevel image thresholding is a well-known technique for image segmentation. Recently, various metaheuristic methods have been proposed for the determination of the thresholds for multilevel image segmentation. These methods are mainly based on metaphors and they have high complexity and their convergences are comparably slow. In this paper, a multilevel image thresholding approach is proposed that simplifies the thresholding problem by using a simple optimization technique instead of metaphor-based algorithms. More specifically, in this paper, Chaotic enhanced Rao (CER) algorithms are developed where eight chaotic maps namely Logistic, Sine, Sinusoidal, Gauss, Circle, Chebyshev, Singer, and Tent are used. Besides, in the developed CER algorithm, the number of thresholds is determined automatically, instead of manual determination. The performances of the developed CER algorithms are evaluated based on different statistical analysis metrics namely BDE, PRI, VOI, GCE, SSIM, FSIM, RMSE, PSNR, NK, AD, SC, MD, and NAE. The experimental works and the related evaluations are carried out on the BSDS300 dataset. The obtained experimental results demonstrate that the proposed CER algorithm outperforms the compared methods based on PRI, SSIM, FSIM, PSNR, RMSE, AD, and NAE metrics. In addition, the proposed method provides better convergence regarding speed and accuracy.
Competing Interests: Conflict of interestThe authors declare that they have no conflicts of interest.
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Databáze: MEDLINE
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