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Automatic Container Code Recognition System Based on Geometrical Clustering and Spatial Structure Template Matching

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Communications, Signal Processing, and Systems (CSPS 2017)

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 463))

Abstract

At present, at most ports the code of each container is registered manually, which is of a great potential safety hazard and inefficient. In this paper we present a container-code recognition system, which use the geometrical clustering of connected component extracted by MSER descriptor and spatial structure template matching for location and various CNN-classifiers for identification. Experiments confirmed the robustness and accurateness of the recognition algorithm on real images from ports.

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Acknowledgments

This work was supported by the Natural Science Foundation of Shandong Province, Grant No. ZR2015YL020.

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Authors and Affiliations

  1. Institute of Oceanographic Instrumentation, Shandong Academy of Science, Qingdao, 266001, China

    Lin Cao, Zhigang Gai, Enxiao Liu, Hao Gao, Hui Li, Lei Yang & Heng Li

Authors
  1. Lin Cao
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  2. Zhigang Gai
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  3. Enxiao Liu
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  4. Hao Gao
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  5. Hui Li
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  6. Lei Yang
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  7. Heng Li
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Corresponding author

Correspondence to Enxiao Liu .

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Editors and Affiliations

  1. Department of Electrical Engineering, University of Texas at Arlington, Arlington, Texas, USA

    Qilian Liang

  2. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Jiasong Mu

  3. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Min Jia

  4. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Wei Wang

  5. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Xuhong Feng

  6. College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China

    Baoju Zhang

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Cite this paper

Cao, L. et al. (2019). Automatic Container Code Recognition System Based on Geometrical Clustering and Spatial Structure Template Matching. In: Liang, Q., Mu, J., Jia, M., Wang, W., Feng, X., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2017. Lecture Notes in Electrical Engineering, vol 463. Springer, Singapore. https://doi.org/10.1007/978-981-10-6571-2_268

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  • DOI: https://doi.org/10.1007/978-981-10-6571-2_268

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6570-5

  • Online ISBN: 978-981-10-6571-2

  • eBook Packages: EngineeringEngineering (R0)

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