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PCNN double step firing mode for image edge detection

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A Correction to this article was published on 25 June 2022

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Abstract

Pulse-coupled Neural Network (PCNN) is a third-generation artificial neural network that requires no training. Neurons in PCNN have two pulse burst modes: firing mode and fire-extinguishing mode. A lot of research has been conducted on achieving image segmentation using the fire-extinguishing mode, yet remains deficient on the characteristics and applications of the firing mode. Through analysis of the firing process of PCNN, we find that the network that works only on the firing mode has the characteristic of image edge detection. Then, we give a mathematical expression for the neuron firing time, and, using the expression, propose an image edge detection algorithm based on the characteristic of PCNN double step firing. Then, we analyze the parameter constraints for achieving PCNN double step firing and provide a method for the self-adaptive setting of the network parameters. To achieve the best edge detection, we conduct mathematical analysis on the relationship between edge detection performance and neighbor coupling, and provide a neighbor template structure and the setting of its values. Our results show that the proposed algorithm can obtain smooth and unbroken single pixels edges, and has nice robustness and efficiency for various kinds of images.

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Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61961037), and the Industrial Support Plan of Education Department of Gansu Province (No. 2021CYZC-30)

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Authors and Affiliations

  1. College of Physics and Electronic Engineering, Northwest Normal University, Lanzhou, 730070, Gansu, People’s Republic of China

    Xiangyu Deng, Yahan Yang & Huan Zhang

  2. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, Gansu, People’s Republic of China

    Yide Ma

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  1. Xiangyu Deng
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Deng, X., Yang, Y., Zhang, H. et al. PCNN double step firing mode for image edge detection. Multimed Tools Appl 81, 27187–27213 (2022). https://doi.org/10.1007/s11042-022-12725-2

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