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http://dx.doi.org/10.5302/J.ICROS.2011.17.6.587

Automatic Thresholding Selection for Image Segmentation Based on Genetic Algorithm  

Lee, Byung-Ryong (University of Ulsan)
Truong, Quoc Bao (University of Ulsan)
Pham, Van Huy (University of Ulsan)
Kim, Hyoung-Seok (University of Ulsan)
Publication Information
Journal of Institute of Control, Robotics and Systems / v.17, no.6, 2011 , pp. 587-595 More about this Journal
Abstract
In this paper, we focus on the issue of automatic selection for multi-level threshold, and we greatly improve the efficiency of Otsu's method for image segmentation based on genetic algorithm. We have investigated and evaluated the performance of the Otsu and Valley-emphasis threshold methods. Based on this observation we propose a method for automatic threshold method that segments an image into more than two regions with high performance and processing in real-time. Our paper introduced new peak detection, combines with evolution algorithm using MAGA (Modified Adaptive Genetic Algorithm) and HCA (Hill Climbing Algorithm), to find the best threshold automatically, accurately, and quickly. The experimental results show that the proposed evolutionary algorithm achieves a satisfactory segmentation effect and that the processing time can be greatly reduced when the number of thresholds increases.
Keywords
image segmentation; automatic threshold; otsu's method; valley-emphasis method; genetic algorithm;
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Times Cited By KSCI : 2  (Citation Analysis)
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