Evolution-Based Vision Algorithm with Fuzzy Fitness Function for Obstacle Detection

Abstract : The proposed algorithm is a fast evolution-based vision technique for real-time obstacle detection. Based on the Parisian approach, our algorithm evolves a population of 3D particles which constitutes a three-dimensional representation of the scene. Evolution is controlled by a fuzzy fitness function able to deal with uncertain camera measurements, and uses classical evolutionary operators. The result of the algorithm is a set of 3D particles gathered on the surfaces of obstacles.
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Communication dans un congrès
META: Metaheuristics and Nature Inspired Computing, Oct 2010, Djerba, Tunisia. 1st International Conference on Metaheuristics and Nature Inspired Computing, pp.51-52, 2010
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Contributeur : Haythem Ghazouani <>
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Haythem Ghazouani, Tagina Moncef, René Zapata. Evolution-Based Vision Algorithm with Fuzzy Fitness Function for Obstacle Detection. META: Metaheuristics and Nature Inspired Computing, Oct 2010, Djerba, Tunisia. 1st International Conference on Metaheuristics and Nature Inspired Computing, pp.51-52, 2010. 〈lirmm-00954358〉

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