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Object Recognition Based on Superposition Proportion in Binary Images

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Intelligent Data Analysis and Applications (ECC 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 535))

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Abstract

This paper describes an approach for recognizing object. Object recognition plays an important role in intelligent system. But usually the existing methods can hardly deal with occluded, shadowy and blurred images. A novel approach is proposed in this paper. This proposed approach based on prior model recognize the main regions and direction of object to adjust the model on size and direction. Finally the proposed approach compute the superposition proportion similarity, between model and object. The experimental result shows that this proposed approach is robust to scenarios containing occlusion, cluttering and low contrast edges.

This work was supported by the National Natural Science Foundation of China under Grants No. 61571346, 61305040.

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Author information

Authors and Affiliations

  1. School of Aerospace Science and Technology, Xidian University, Xi’an, 710071, China

    Yan Zheng, Baolong Guo & Jing Ma

Authors
  1. Yan Zheng
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  2. Baolong Guo
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  3. Jing Ma
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Corresponding author

Correspondence to Jing Ma .

Editor information

Editors and Affiliations

  1. College of Information Science and Engineering, Fujian University of Technology , Fuzhou, Fujian, China

    Jeng-Shyang Pan

  2. Faculty of Electrical Engineering and Computer Science, VŠB-Technical University of Ostrava , Ostrava-Poruba, Moravskoslezsky, Czech Republic

    Václav Snášel

  3. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, China

    Tien-Wen Sung

  4. College of Information Science and Engineering, Fujian University of Technology, Fuzhou, China

    Xiao Dong Wang

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Zheng, Y., Guo, B., Ma, J. (2017). Object Recognition Based on Superposition Proportion in Binary Images. In: Pan, JS., Snášel, V., Sung, TW., Wang, X. (eds) Intelligent Data Analysis and Applications. ECC 2016. Advances in Intelligent Systems and Computing, vol 535. Springer, Cham. https://doi.org/10.1007/978-3-319-48499-0_9

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  • DOI: https://doi.org/10.1007/978-3-319-48499-0_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-48498-3

  • Online ISBN: 978-3-319-48499-0

  • eBook Packages: EngineeringEngineering (R0)

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