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An Improved Watershed Image Segmentation Algorithm Combining With a New Entropy Evaluation Criterion

机译：一种改进的流域图像分割算法与新熵评估标准相结合

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摘要

An improved watershed image segmentation algorithm is proposed to solve the problem of over-segmentation by classical watershed algorithm. The new algorithm combines region growing with classical watershed algorithm. The key to region growing lies in choosing a growing threshold to reach a desired result of image segmentation. An entropy evaluation criterion is constructed to determine the optimal threshold. Considering the entropy evaluation criterion as an objective function, the particle swarm optimization algorithm is employed to search global optimization of the objective function. Experimental results show that this new algorithm can solve the problem of over-segmentation effectively.
机译：提出了一种改进的流域图像分割算法来解决经典流域算法过分分割问题。新算法结合了古典流域算法的繁殖。区域生长的关键在于选择不断增长的阈值以达到图像分割的期望结果。构建熵评估标准以确定最佳阈值。考虑到熵评估标准作为目标函数，采用粒子群优化算法来搜索目标函数的全局优化。实验结果表明，这种新算法有效地解决了过分的问题。

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