Otsu image threshold segmentation method based on new genetic algorithm
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摘要: 最大类间方差（Otsu）图像分割法是常用的一种基于统计原理的图像阈值分割方法。为了改善Otsu耗时较多、分割的精度低、易产生图像误分割等不足，将猴王遗传算法与Otsu算法结合，运用猴王遗传算法的原理，寻找图像灰度的最大类间方差，即最佳阈值。结果表明，结合后的方法不仅提高了图像的分割质量、缩短了运算时间，而且非常适合图像的实时处理。Abstract: Maximum between-class variance (Otsu) image segmentation method is a common image threshold segmentation method based on statistical theory, but Otsu image segmentation method has some disadvantages, such as more time-consuming, low segmentation accuracy and false image segmentation. Combining the principles of monkey king genetic algorithms, with Otsu algorithm, image gray, just as optimal threshold, was found. The results show that combined method not only improves the quality of image segmentation but also reduce the computation time. It is very suitable for real-time image processing.
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