Level Set method in standing tree image segmentation based on particle swarm optimization

For the intelligent pruning machine, a machine vision system is pre-requisite. Standing tree image segmentation is a key step for the machine vision system. An efficient scheme for tree image segmentation was proposed according to the need of the machine vision system of the intelligent pruning machine. The scheme is a level set method based on particle swarm optimization. According to principal of the level set method, the image segmentation is formulated as one of optimization problems. The energy function is taken as the segmentation quality criteria, which consists of an internal energy term that penalizes the deviation of the level set function from a signed distance function, and an external energy term that drives the motion of the zero level set toward the desired image feature, such as object boundaries. In this paper, the method used particle swarm optimization to solve the optimization problems that is different from the ordinary level set method that uses the partial differential equation method in some literatures. In experiments, tree images with different background are selected to test the efficiency of the scheme that presented in this paper. In order to test the antimonies performance of the scheme that presented in this paper, a tree image added Gaussian white noise is selected. From the results of the tree image segmentation, the scheme that presented in this paper is more efficiently. The experimental results demonstrate the scheme is more effective and time-saving than the ordinary level set method.

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