Abstract
Finding gradient maximum and local maximum based on multi-scale Canny operator edge detection is really the optimization for two dimension multi-element function. The maximal function gradient or local maximum which was derived by the traditional analytics is approximate and local. A new multi-scale edge detection algorithm is proposed by a genetic optimum searching algorithm. To upgrade the genetic algorithm convergence about edge detection, an improved GA+SA+TABU is used in order to overcome the defects of local searching in the general genetic algorithm and upgrade the whole resolution. Alternating optimization tactics are utilized by combining the general algorithm and heuristic searching methods. The experimental results show that the proposed algorithm applied to IR target image segmentation could result in copious details, single edge and exact location.
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