PCNN Model Analysis and Its Automatic Parameters Determination in Image Segmentation and Edge Detection

DENG Xiangyu; MA Yide

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Article Navigation > Chinese Journal of Electronics > 2014 > 23(1): 97-103

DENG Xiangyu and MA Yide, “PCNN Model Analysis and Its Automatic Parameters Determination in Image Segmentation and Edge Detection,” Chinese Journal of Electronics, vol. 23, no. 1, pp. 97-103, 2014,

Citation:

DENG Xiangyu and MA Yide, “PCNN Model Analysis and Its Automatic Parameters Determination in Image Segmentation and Edge Detection,” Chinese Journal of Electronics, vol. 23, no. 1, pp. 97-103, 2014,

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PCNN Model Analysis and Its Automatic Parameters Determination in Image Segmentation and Edge Detection

DENG Xiangyu^1,2,
MA Yide¹

1.
School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, China;
2.
School of Electronics and Information Engineering, Lanzhou Institute of Technology, Lanzhou, Gansu 730050, China

Funds: This work is supported by the National Natural Science Foundation of China (No.61175012), the Specialized Research Fund for the Doctoral Program of Higher Education of China (No.20110211110026), and the Education Department Graduate Tutor Project of Gansu, China (No.1014-02).

Received Date: 2012-03-01
Rev Recd Date: 2013-01-01
Publish Date: 2014-01-05

Abstract

Abstract

The Pulse coupled neural network (PCNN) has been widely used in digital image processing, but the automatic parameters determination is still a difficult aspect, which becomes the focus of PCNN research. In this paper, by the classical solution to difference equations and the time-domain analysis of PCNN model, we provide the expressions of the firing time and the firing period of neurons, and reveal the "mathematics firing" phenomenon of PCNN. Based on this, we propose a new method of automatic parameters determination based on both eliminating the "mathematics firing" and getting the highest efficiency of PCNN. We also present an edge detection model on the basis of image segmentation of PCNN and a method to determine automatically the parameters of the model. Experimental results prove the validity and efficiency of our proposed algorithm for the segmentation and the edge detection of the test images.
- Pulse coupled neural network (PCNN) model,
- Neuron firing mechanism,
- Parameters determination,
- Image segmentation,
- Edge detection model

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References(30)

References

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