Otsu image threshold segmentation method based on new genetic algorithm

WANG Hongwen; LIANG Yanyan; WANG Zhihua

doi:10.7510/jgjs.issn.1001-3806.2014.03.017

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.

Otsu image threshold segmentation method based on new genetic algorithm

1. School of Control Science and Engineering, Hebei University of Technology, Tianjin 300130, China

Received Date: 2013-05-20

Accepted Date: 2013-07-31

Available Online: 2014-05-25

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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Reference (10)

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Otsu image threshold segmentation method based on new genetic algorithm

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