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International Conference on Intelligent Computing

ICIC 2015: Intelligent Computing Theories and Methodologies pp 165–169Cite as

A BRMF-Based Model for Missing-Data Estimation of Image Sequence

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9226))

Abstract

How to effectively deal with occlusion is an important step of structure from motion (SFM). In this paper, an accurate missing data estimation method is proposed by combining Bayesian robust matrix factorization (BRMF) and particle swarm optimization (PSO). Experimental results on several widely used image sequences demonstrate the effectiveness and feasibility of the proposed algorithm.

The work was supported by grants from National Natural Science Foundation of China (Nos. 61370109, 61272025), a grant from Natural Science Foundation of Anhui Province (No. 1308085MF85), and 2013 Zhan-Li Sun’s Technology Foundation for Selected Overseas Chinese Scholars from department of human resources and social security of Anhui Province (Project name: Research on structure from motion and its application on 3D face reconstruction).

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Author information

Authors and Affiliations

  1. School of Electrical Engineering and Automation, Anhui University, Hefei, China

    Yang Liu, Zhan-Li Sun, Yun Jing, Yang Qian & De-Xiang Zhang

Authors
  1. Yang Liu
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  2. Zhan-Li Sun
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  3. Yun Jing
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  4. Yang Qian
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  5. De-Xiang Zhang
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Corresponding author

Correspondence to Zhan-Li Sun .

Editor information

Editors and Affiliations

  1. Tongji University, Shanghai, China

    De-Shuang Huang

  2. University of Ulsan, Ulsan, Korea (Republic of)

    Kang-Hyun Jo

  3. Liverpool John Moores University, Liverpool, United Kingdom

    Abir Hussain

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© 2015 Springer International Publishing Switzerland

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Liu, Y., Sun, ZL., Jing, Y., Qian, Y., Zhang, DX. (2015). A BRMF-Based Model for Missing-Data Estimation of Image Sequence. In: Huang, DS., Jo, KH., Hussain, A. (eds) Intelligent Computing Theories and Methodologies. ICIC 2015. Lecture Notes in Computer Science(), vol 9226. Springer, Cham. https://doi.org/10.1007/978-3-319-22186-1_16

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  • DOI: https://doi.org/10.1007/978-3-319-22186-1_16

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-22185-4

  • Online ISBN: 978-3-319-22186-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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