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Image Thresholding Using TRIBES, a Parameter-Free Particle Swarm Optimization Algorithm

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5313)

Abstract

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.

Keywords

Particle Swarm Optimization Image Segmentation Particle Swarm Optimization Algorithm Good Location Convergence Curve 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  1. 1.Laboratoire Images, Signaux et Systèmes Intelligents, LiSSi, E.A 3956Université de Paris 12CréteilFrance

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