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

机译:结合新的熵评价准则的改进分水岭图像分割算法

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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:提出了一种改进的分水岭图像分割算法,以解决经典分水岭算法过分分割的问题。新算法将区域增长与经典分水岭算法相结合。区域增长的关键在于选择增长阈值以达到所需的图像分割结果。构造熵评估标准以确定最佳阈值。以熵评估准则为目标函数,采用粒子群算法对目标函数进行全局优化。实验结果表明,该新算法可以有效解决分割过度的问题。

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