Optimal evolution algorithm for image thresholding

被引：7
|
作者：
Lin Z. [1 ]
Wang Z. [1 ]
Zhang Y. [1 ]
机构：
[1] School of Computer Science and Engineering, South China University of Technology
来源：
Jisuanji Fuzhu Sheji Yu Tuxingxue Xuebao/Journal of Computer-Aided Design and Computer Graphics | 2010年 / 22卷 / 07期
关键词：
Genetic algorithm; Grey-level; Histogram; Image segmentation; Image thresholding;
D O I：
10.3724/SP.J.1089.2010.10907
中图分类号：
学科分类号：
摘要：
An optimal evolution algorithm for image thresholding is proposed based on theories of genetic algorithm. Pixel and threshold are regarded as chromosome and evolution direction, respectively. Assuming the optimal evolution direction exists, the updating model of evolution direction is established. Then by defining the chromosomes' coding rules, initializing the group by simple-random-sampling, and redefining the fitness function and the selection mechanism, the optimal threshold is obtained under the proper crossover rate and mutation rate. The rationalities of the assumption and the updating model have been analyzed in this paper. The experimental results show that the assumption and the updating model are proper; the proposed algorithm is robust and effective.
引用
收藏
页码：1201 / 1206
页数：5
相关论文
共 19 条
  • [1] Zhang Y.J., A survey on evaluation methods for image segmentation, Pattern Recognition, 29, 8, pp. 1335-1346, (1996)
  • [2] Sezgin M., Sankur B., Survey over image thresholding techniques and quantitative performance evaluation, Journal of Electronic Imaging, 13, 1, pp. 146-165, (2004)
  • [3] Tizhoosh H.R., Image thresholding using type ii fuzzy sets, Pattern Recognition, 38, 12, pp. 2363-2372, (2005)
  • [4] Davies E.R., Stable bi-level and multi-level thresholding of images using a new global transformation, IET Computer Vision, 2, 2, pp. 60-74, (2008)
  • [5] Dong L.J., Yu G., Ogunbona P., Et al., An efficient iterative algorithm for image thresholding, Pattern Recognition Letters, 29, 9, pp. 1311-1316, (2008)
  • [6] Tao W.B., Jin H., Zhang Y.M., Et al., Image thresholding using graph cuts, IEEE Transactions on Systems, Man and Cybernetics, 38, 5, pp. 1181-1195, (2008)
  • [7] Wang S.T., Chung F.L., Xiong F.S., A novel image thresholding method based on Parzen window estimate, Pattern Recognition, 41, 1, pp. 117-129, (2008)
  • [8] Malisia A.R., Tizhoosh H.R., Image thresholding using ant colony optimization, Proceedings of the 3rd Canadian Conference on Computer and Robot Vision, pp. 26-31, (2006)
  • [9] Lei B., Fan J.L., Parameter selection of generalized fuzzy entropy-based thresholding segmentation method with particle swarm optimization, Proceedings of International Conference on Intelligent Information Hiding and Multimedia Signal Processing, pp. 901-904, (2008)
  • [10] Hammouche K., Diaf M., Siarry P., A multilevel automatic thresholding method based on a genetic algorithm for a fast image segmentation, Computer Vision and Image Understanding, 109, 2, pp. 163-175, (2008)
12