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Adaptive Ant Colony algorithm for subpixel edge de
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Ant colony algorithm in 1992 by the Italian scholar M. Bionic Dorigo and his colleagues by the process of ants foraging route choice behavior inspired evolutionary algorithm proposed. In the long-term study in which they found that although there is no visual ants, but always able to find the shortest path distance food source in search of food in the process. At the initial stage, ant walking and release pheromones (Pheromone) on a random path, pheromones will continue with the delayed time of volatilization. Ants on a path to complete a search for the longer elapsed time, the volatile pheromones longer time, less residue. Through shorter path found so pheromones on the path in the same time has been greatly volatilized compensation, increasing the intensity of the total hormone. Exchange of information and mutual cooperation between ants through its search path pheromones strength to achieve, and each ant pheromones have the ability to perceive this stre
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Computer Vision Applications with C#
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Moving on from Part II, this article presents a tracking algorithm. The Centroid tracker presented in Part II was not actually a tracking algorithm but a fancy trick. All the "tracker" was doing was to find the centroid of the backprojected image and put a bounding box around it - this is not tracking by definition. So this article presents a very well known tracking algorithm in Computer Vision community called Meanshift Tracking. This article is based on a series of research papers by Comaniciu Et Al. [1],[2],[3].
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