원문정보
보안공학연구지원센터(IJSIP)
International Journal of Signal Processing, Image Processing and Pattern Recognition
Vol.7 No.5
2014.10
pp.249-258
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
In order to use pulse coupled neural networks (PCNN) for precise automatic image segmentation, we propose an improved PCNN model. We first establish a connection weight matrix based on the image local gray correlation and on the Euclid distance. We then used the minimum variance ratio criterion to automatically determine PCNN cycle times, and achieve automatic image segmentation. The simulation results show that this method can automatically determine the number of iterations PCNN, and that it is highly feasible and better segmentation effect.
목차
Abstract
1. Introduction
2. PCNN Model and the Theory of Image Segmentation
2.1 PCNN Model
2.2 The Theory of Image Segmentation Utilized PCNN Model
3. The Establishment of Gray Correlation Weight Matrix
4. The Least Variable Ratio Principle
4.1 The Establishment of the Least Variable Ratio Principle
4.2. The Judgement of PCNN Iteration Times
5. Experimental Results and the Analysis of the Problems
5.1 Experimental Results and Analysis
5.2 The Analysis of the Exist Problems
6. Conclusion
Acknowledgements
References
1. Introduction
2. PCNN Model and the Theory of Image Segmentation
2.1 PCNN Model
2.2 The Theory of Image Segmentation Utilized PCNN Model
3. The Establishment of Gray Correlation Weight Matrix
4. The Least Variable Ratio Principle
4.1 The Establishment of the Least Variable Ratio Principle
4.2. The Judgement of PCNN Iteration Times
5. Experimental Results and the Analysis of the Problems
5.1 Experimental Results and Analysis
5.2 The Analysis of the Exist Problems
6. Conclusion
Acknowledgements
References
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