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CPSOGSA for Multilevel Image Thresholding

version 1.1 (101 KB) by Sajad Ahmad Rather
CPSOGSA is employed to find the optimal pixels in the benchmark images

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Updated 07 Jul 2021

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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

Cite As

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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To view or report issues in this GitHub add-on, visit the GitHub Repository.
To view or report issues in this GitHub add-on, visit the GitHub Repository.