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Memristor Crossbar Array Based ACO For Image Edge Detection

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Abstract

Memristor provides an available way to design and deploy swarm intelligence. As a typical swarm intelligence algorithm, ant colony optimization is implemented by the memristor crossbar array to make image edge detection in this paper. Firstly, a non-linear voltage-controlled memristor model with a relaxation term is proposed. Then, an improved ant colony optimization with padding strategy is designed. Thirdly, a memristor crossbar array with external control circuits is designed to implement ant colony optimization for image edge detection, which offers high device density and parallel computing. In the course of ant colony optimization based image edge detection deployed by memristor crossbar array, the threshold to generating edges can be directly chosen as the mean of the final conductance matrix. On the one hand, experiment results show that more delicate edges can be detected by proposed method compared to holistically-nested edge detection based on neural networks. On the other hand, Figure of merit of proposed method is better than that of Sobel operator.

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Acknowledgements

This work are supported by Research Fund for International Young Scientists of National Natural Science Foundation of China (NSFC Grant No. 61550110248), Sichuan Science and Technology Program (Grant No. 2019YFG0190) and Research on Sino-Tibetan multi-source information acquisition, fusion, data mining and its application (Grant No. H04W170186).

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Authors and Affiliations

  1. School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China

    Yongbin Yu, Quanxin Deng & Liyong Ren

  2. School of Information Science and Technology, Tibet University, Lhasa, China

    Nyima Tashi

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  1. Yongbin Yu
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  2. Quanxin Deng
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  3. Liyong Ren
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  4. Nyima Tashi
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Correspondence to Yongbin Yu.

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Yu, Y., Deng, Q., Ren, L. et al. Memristor Crossbar Array Based ACO For Image Edge Detection. Neural Process Lett 51, 1891–1905 (2020). https://doi.org/10.1007/s11063-019-10179-6

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