An image segmentation method using automatic threshold based on improved genetic selecting algorithm
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In this paper, an image segmentation method using automatic threshold based on improved genetic selecting algorithm is presented. Optimal threshold for image segmentation is converted into an optimization problem in this new method. In order to achieve good effects for image segmentation, the optimal threshold is solved by using optimizing efficiency of improved genetic selecting algorithm that can achieve a global optimum. The genetic selecting algorithm is optimized by using simulated annealing temperature parameters to achieve appropriate selective pressures. Encoding, crossover, mutation operator and other parameters of genetic selecting algorithm are improved moderately in this method. It can overcome the shortcomings of the existing image segmentation methods, which only consider pixel gray value without considering spatial features and large computational complexity of these algorithms. Experiment results show that the new algorithm greatly reduces the optimization time, enhances the anti-noise performance of image segmentation, and improves the efficiency of image segmentation. Experimental results also show that the new algorithm can get better segmentation effect than that of Otsu’s method when the gray-level distribution of the background follows normal distribution approximately, and the target region is less than the background region. Therefore, the new method can facilitate subsequent processing for computer vision, and can be applied to realtime image segmentation.
Keywordsimproved genetic selecting algorithm image segmentation automatic threshold Otsu
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- 6.Boykov, Yu. and Jolly, M.-P., Interactive graph cuts for optimal boundary & region segmentation of objects in N-D images, Int. Conf. Comput. Vision, 2001, vol. 1, pp. 105–112.Google Scholar
- 7.Li, H., Lai, Z.A., Lei, J.W., Image threshold segmentation algorithm based on histogram statistical property, Appl. Mech. Mat., 2014, vol. 644—650, pp. 4027–4030.Google Scholar
- 15.Zhiwen, W. and Shaozi, L., A fast watermarking algorithm based on quantum evolutionary algorithm, J. Optoelectron., Laser, 2010, vol. 21, pp. 737–742.Google Scholar
- 16.Grady, L., Random walks for image segmentation, Pattern Anal. Mach. Intell., 2007, vol. 28, pp. 1–17.Google Scholar