Optimal Threshold Computing in Automatic Image Thresholding using Adaptive Particle Swarm Optimization
histogram which can be modeled as a mixture of two Gaussian density functions, estimating these densities inpractice is not simply feasible. The objective of this paper is to use adaptive particle swarm optimization (APSO) forthe suboptimal estimation of the means and variances of these two Gaussian...
| Autores: | , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2012 |
| País: | México |
| Institución: | UNIVERSIDAD NACIONAL AUTÓNOMA DE MÉXICO |
| Repositorio: | Journal of Applied Research and Technology |
| Idioma: | español |
| OAI Identifier: | oai:ojs2.localhost:article/361 |
| Acceso en línea: | https://jart.icat.unam.mx/index.php/jart/article/view/361 |
| Access Level: | acceso abierto |
| Palabra clave: | histogram-based thresholding adaptive particle swarm optimization genetic algorithm fitness function object and background detection |
| Sumario: | histogram which can be modeled as a mixture of two Gaussian density functions, estimating these densities inpractice is not simply feasible. The objective of this paper is to use adaptive particle swarm optimization (APSO) forthe suboptimal estimation of the means and variances of these two Gaussian density functions; then, the computationof the optimal threshold value is straightforward. The comparisons of experimental results in a wide range of complexbimodal images show that this proposed thresholding algorithm presents higher correct detection rate of object andbackground in comparison to the other methods including Otsu’s method and estimating the parameters of Gaussiandensity functions using genetic algorithm (GA). Meanwhile, the proposed thresholding method needs lower executiontime than the PSO-based method, while it shows a little higher correct detection rate of object and background, withlower false acceptance rate and false rejection rate. |
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