Optimal Threshold Computing in Automatic Image Thresholding using Adaptive Particle Swarm Optimization
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| Natura: | Artículo científico |
| Lingua: | en |
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Universidad Nacional Autónoma de México
2012
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| _version_ | 1876428341842542592 |
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| author | P. Moallem |
| author_facet | P. Moallem |
| contents | Optimal Threshold Computing in Automatic Image Thresholding using Adaptive Particle Swarm Optimization P. Moallem N. Razmjooy Ingeniería Histogram fitness function genetic algorithm based thresholding object and background detection Selecting an optimal threshold value is the most important step in image thresholding algorithms. For a bimodal histogram which can be modeled as a mixture of two Gaussian density functions, estimating these densities in practice is not simply feasible. The objective of this paper is to use adaptive particle swarm optimization (APSO) for the suboptimal estimation of the means and variances of these two Gaussian density functions; then, the computation of the optimal threshold value is straightforward. The comparisons of experimental results in a wide range of complex bimodal images show that this proposed thresholding algorithm presents higher correct detection rate of object and background in comparison to the other methods including Otsu’s method and estimating the parameters of Gaussian density functions using genetic algorithm (GA). Meanwhile, the proposed thresholding method needs lower execution time than the PSO-based method, while it shows a little higher correct detection rate of object and background, with lower false acceptance rate and false rejection rate. 2012 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47425122006 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.5 Vol.10 |
| format | Artículo científico |
| id | redalyc_47425122006 |
| institution | Redalyc |
| language | en |
| publishDate | 2012 |
| publisher | Universidad Nacional Autónoma de México |
| spellingShingle | Optimal Threshold Computing in Automatic Image Thresholding using Adaptive Particle Swarm Optimization P. Moallem Ingeniería Histogram fitness function genetic algorithm based thresholding object and background detection Optimal Threshold Computing in Automatic Image Thresholding using Adaptive Particle Swarm Optimization P. Moallem N. Razmjooy Ingeniería Histogram fitness function genetic algorithm based thresholding object and background detection Selecting an optimal threshold value is the most important step in image thresholding algorithms. For a bimodal histogram which can be modeled as a mixture of two Gaussian density functions, estimating these densities in practice is not simply feasible. The objective of this paper is to use adaptive particle swarm optimization (APSO) for the suboptimal estimation of the means and variances of these two Gaussian density functions; then, the computation of the optimal threshold value is straightforward. The comparisons of experimental results in a wide range of complex bimodal images show that this proposed thresholding algorithm presents higher correct detection rate of object and background in comparison to the other methods including Otsu’s method and estimating the parameters of Gaussian density functions using genetic algorithm (GA). Meanwhile, the proposed thresholding method needs lower execution time than the PSO-based method, while it shows a little higher correct detection rate of object and background, with lower false acceptance rate and false rejection rate. 2012 artículo científico 1665-6423 https://www.redalyc.org/articulo.oa?id=47425122006 en http://www.redalyc.org/revista.oa?id=474 Journal of Applied Research and Technology application/pdf Universidad Nacional Autónoma de México Journal of Applied Research and Technology (México) Num.5 Vol.10 |
| title | Optimal Threshold Computing in Automatic Image Thresholding using Adaptive Particle Swarm Optimization |
| topic | Ingeniería Histogram fitness function genetic algorithm based thresholding object and background detection |
| url | https://www.redalyc.org/articulo.oa?id=47425122006 |