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License Plate Extraction Using Spiking Neural Networks

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Intelligent Computing Theories and Methodologies (ICIC 2015)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9225))

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Abstract

In this paper, we present an algorithm for license plate detection and extraction using spiking neural networks (SNNs). We propose an SNN for the detection of license plate by simulating the color perception principle in human beings’ visual system, where synchronization of spiking trains are employed as a color detection function and used to detect the license plate according to the difference of color in the license plate’s patch and those in the other image patches. By doing so, we can extract those image regions that are likely to be license plates. And then we use another SNN to produce the edge images of these candidates by simulating the receptive field of orientation in human beings’ visual cortex. Finally, we extract the license plate from these candidates according to the texture difference between a real license plate image and the distracters, where the numbers of strokes in image rows are served as cues for the texture difference. The experimental results show that the proposed biological inspired SNNs are valid in the detection and extraction of license plate.

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Acknowledgement

This work was supported by the Science-Technology Project of Education Bureau of Fujian Province, China (Grant No. JA13073), the Natural Science Foundation of Fujian Province, China (Grant No. 2014J01224), and the National Natural Science Foundation of China (Grant No. 61179011).

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Authors and Affiliations

  1. College of Photonic and Electronic Engineering, Fujian Normal University, Fuzhou, 350108, Fujian, China

    Qian Du, LiJuan Chen, RongTai Cai, Peng Zhu, TianShui Wu & QingXiang Wu

Authors
  1. Qian Du
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  2. LiJuan Chen
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  3. RongTai Cai
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  4. Peng Zhu
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  5. TianShui Wu
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  6. QingXiang Wu
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Corresponding author

Correspondence to RongTai Cai .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. Polytecnic of Bari, Bari, Italy

    Vitoantonio Bevilacqua

  3. University of Wollongong, North Wollongong, New South Wales, Australia

    Prashan Premaratne

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Du, Q., Chen, L., Cai, R., Zhu, P., Wu, T., Wu, Q. (2015). License Plate Extraction Using Spiking Neural Networks. In: Huang, DS., Bevilacqua, V., Premaratne, P. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9225. Springer, Cham. https://doi.org/10.1007/978-3-319-22180-9_36

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  • DOI: https://doi.org/10.1007/978-3-319-22180-9_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22179-3

  • Online ISBN: 978-3-319-22180-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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