CPSOGSA for Multilevel Image Thresholding

CPSOGSA is employed to find the optimal pixels in the benchmark images
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Aggiornato 7 lug 2021
This work introduces a new image segmentation method based on the constriction coefficient-based particle swarm optimization and gravitational search algorithm (CPSOGSA). The random samples of the image act as searcher agents of the CPSOGSA algorithm. The optimal number of thresholds is determined using Kapur's entropy method. The effectiveness and applicability of CPSOGSA in image segmentation is accomplished by applying it to five standard images from the USC-SIPI image database, namely Aeroplane, Cameraman, Clock, Lena, and Pirate.
This is the source code of the paper:
Rather, S. A., & Bala, P. S. (2021), “Constriction Coefficient Based Particle Swarm Optimization and Gravitational Search Algorithm for Multilevel Image Thresholding”, Expert Systems, https://doi.org/10.1111/exsy.12717, Wiley, SCIE (I.F = 2.587).
If you have no access to the paper, please drop me an email at sajad.win8@gmail.com and I will obviously send you the paper. All of the source codes and extra information as well as more optimization techniques can be found in my Github page at https://github.com/SajadAHMAD1.
My other Social Media Link(s)/Accounts:
13) Gmail: sajad.win8@gmail.com

Cita come

Rather, Sajad Ahmad, and P. Shanthi Bala. “Constriction Coefficient Based Particle Swarm Optimization and Gravitational Search Algorithm for Multilevel Image Thresholding.” Expert Systems, Wiley, May 2021, doi:10.1111/exsy.12717.

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Versione Pubblicato Note della release
1.1

See release notes for this release on GitHub: https://github.com/SajadAHMAD1/CPSOGSA-for-Multilevel-Image-Thresholding/releases/tag/1.1

Per visualizzare o segnalare problemi su questo componente aggiuntivo di GitHub, visita GitHub Repository.
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