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.
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Cooren, Y., Nakib, A., Siarry, P. (2008). Image Thresholding Using TRIBES, a Parameter-Free Particle Swarm Optimization Algorithm. In: Maniezzo, V., Battiti, R., Watson, JP. (eds) Learning and Intelligent Optimization. LION 2007. Lecture Notes in Computer Science, vol 5313. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92695-5_7
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DOI: https://doi.org/10.1007/978-3-540-92695-5_7
Publisher Name: Springer, Berlin, Heidelberg
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