Traditional two-dimensional Otsu algorithm has several drawbacks; that is, the sum of probabilities of target and background is\napproximate to 1 inaccurately, the details of neighborhood image are not obvious, and the computational cost is high. In order\nto address these problems, a method of fast image segmentation using two-dimensional Otsu based on estimation of distribution\nalgorithm is proposed. Firstly, in order to enhance the performance of image segmentation, the guided filtering is employed to\nimprove neighborhood image template instead of mean filtering. Additionally, the probabilities of target and background in twodimensional\nhistogram are exactly calculated to get more accurate threshold. Finally, the trace of the interclass dispersion matrix\nis taken as the fitness function of estimation of distributed algorithm, and the optimal threshold is obtained by constructing and\nsampling the probability model. Extensive experimental results demonstrate that our method can effectively preserve details of the\ntarget, improve the segmentation precision, and reduce the running time of algorithms.
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