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Partial Alignment of Data Sets Based on Fast Intrinsic Feature Match

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Knowledge Science, Engineering and Management (KSEM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11775))

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Abstract

Point Feature Histograms (PFH) is a statistic and geometric invariant descriptor that has been widely used in shape analysis. Current PFH based feature extraction methods are highly affected by the time scale and become less effective for unbalanced cases, which limits their performance. In this paper, we focus on finding a framework for partial registration by an adaptive partition of point set algorithm. Firstly, we propose an adaptive partition method base on PFH coding. Secondly, we conduct a series of fast parallel implementations for efficiency. Thirdly, we plug in the PFH based partition method and trimmed strategy to our modified iterative closest point method. Experiments demonstrate that our algorithms are robust and stable.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China under Grant No. 11771276.

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Authors and Affiliations

  1. Department of Mathematics, School of Science, Shanghai University, Shanghai, China

    Yaxin Peng, Naiwu Wen & Xiaohuang Zhu

  2. Department of Computer Science, East China Normal University, Shanghai, China

    Chaomin Shen

Authors
  1. Yaxin Peng
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  2. Naiwu Wen
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  3. Xiaohuang Zhu
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  4. Chaomin Shen
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Corresponding author

Correspondence to Chaomin Shen .

Editor information

Editors and Affiliations

  1. University of Piraeus, Piraeus, Greece

    Christos Douligeris

  2. University of Vienna, Vienna, Austria

    Dimitris Karagiannis

  3. University of Piraeus, Piraeus, Greece

    Dimitris Apostolou

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Peng, Y., Wen, N., Zhu, X., Shen, C. (2019). Partial Alignment of Data Sets Based on Fast Intrinsic Feature Match. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_32

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  • DOI: https://doi.org/10.1007/978-3-030-29551-6_32

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-29550-9

  • Online ISBN: 978-3-030-29551-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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