Image Thresholding Using TRIBES, a Parameter-Free Particle Swarm Optimization Algorithm
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Finding the optimal threshold(s) for an image with a multimodal histogram is described in classical literature as a problem of fitting a sum of Gaussians to the histogram. The fitting problem has been shown experimentally to be a nonlinear minimization problem with local minima. In this paper, we propose to reduce the complexity of the method, by using a parameter-free particle swarm optimization algorithm, called TRIBES which avoids the initialization problem. It was proved efficient to solve nonlinear and continuous optimization problems. This algorithm is used as a “black-box” system and does not need any fitting, thus inducing time gain.
KeywordsParticle Swarm Optimization Image Segmentation Particle Swarm Optimization Algorithm Good Location Convergence Curve
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- 4.Kennedy, J., Eberhart, R.C.: Particle Swarm Optimization. In: Proc. IEEE Int. Conf. On Neural Networks, WA, Australia, pp. 1942–1948 (1995)Google Scholar
- 5.Clerc, M.: Particle Swarm Optimization. International Scientific and Technical Encyclopaedia (2006)Google Scholar
- 8.Nawrocki, M., Dohler, M., Aghvami, A.H.: Understanding UMTS radio network modelling, Theory and Practice. Wiley, Chichester (2006)Google Scholar
- 9.Nakib, A., Cooren, Y., Oulhadj, H., Siarry, P.: Magnetic resonance image segmentation based on two-dimensional exponential entropy and a parameter free PSO. In: Proceedings of the 8th International Conference on Artificial Evolution, Tours, France, October 29-31 (2007)Google Scholar
- 12.Gonzales, R.C., Woods, R.E.: Digital image processing. Prentice Hall, Upper Sadler River (2002)Google Scholar
- 13.Particle Swarm Central (2006), http://www.particleswarm.info/Standard_PSO_2006