Earticle

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

키워드

저자

• Hai-Rong Ma [ Faculty of Information Engineering, China University of Geosciences ]
• Xin-Wen Cheng [ Faculty of Information Engineering, China University of Geosciences ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Signal Processing, Image Processing and Pattern Recognition

• 간기

  격월간

• pISSN

  2005-4254

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605