Abstract
On the basis of the OTSU methods study, the paper introduced adaptive genetic algorithm to optimize algorithm and achieve image segmentation. Experiment shows that the speed of the algorithm improves and the quality of segmentation is better. Lay the foundation for image recognition in the following.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zheng, Y.J.: A Survey on Evaluation Methods for Image Segmentation. Pattern Recognition 29(8), 346–352 (1996)
Wu, Y.Q.: The Progress of Methods for Image Threshold Selection in Last Thirty Years (1962-1992). Journal of Data Acquisition & Procession 8(3), 193–201 (1993)
Huang, J.X., Liu, H., Huang, W.: A Threshold Selection Method of Image Segmentation Based on Genetic Algorithms. Journal of Nanjing Normal University (Engineering and Technology Edition) 7(1), 14–17 (2007)
Goldberg, D.: Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley, Pearson, Reading, MA (1989)
Guo, Z., Chen, Y.Z.: Research of Threshold Methods for Image Segmentation. Journal of Communication University of China (Science and Technology) 115(2), 77–82 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Gu, D., Ren, Z. (2011). Image Threshold Segmentation Technology Research Based on Adaptive Genetic Algorithm. In: Wan, X. (eds) Electrical Power Systems and Computers. Lecture Notes in Electrical Engineering, vol 99. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21747-0_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-21747-0_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21746-3
Online ISBN: 978-3-642-21747-0
eBook Packages: EngineeringEngineering (R0)