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Using Gaussian Mixture Model to Fix Errors in SFS Approach Based on Propagation

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Social Computing (ICYCSEE 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 623))

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Abstract

A new Gaussian mixture model is used to improve the quality of propagation method for SFS in this paper. The improved algorithm can overcome most difficulties of propagation SFS method including slow convergence, interdependence of propagation nodes and error accumulation. To slow convergence and interdependence of propagation nodes, stable propagation source and integration path are used to make sure that the reconstruction work of each pixel in the image is independent. A Gaussian mixture model based on prior conditions is proposed to fix the error of integration. Good result has been achieved in the experiment for Lambert composite image of the front illumination.

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Author information

Authors and Affiliations

  1. College of Computer Science and Technology, Heilongjiang University, Harbin, China

    Wenmin Huang, Jiquan Ma & Enbin Zhang

  2. Key Laboratory of Database and Parallel Computing of Heilongjiang Province, Harbin, China

    Jiquan Ma & Enbin Zhang

Authors
  1. Wenmin Huang
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  2. Jiquan Ma
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  3. Enbin Zhang
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Corresponding author

Correspondence to Jiquan Ma .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology , Harbin, China

    Wanxiang Che

  2. Harbin Engineering University , Harbin, China

    Qilong Han

  3. Harbin Institute of Technology , Harbin, China

    Hongzhi Wang

  4. Northeast Forestry University , Harbin, China

    Weipeng Jing

  5. National University of Defense Technology , Changsha, China

    Shaoliang Peng

  6. Harbin Engineering University , Harbin, China

    Junyu Lin

  7. Harbin Univ. of Science and Technology , Harbin, China

    Guanglu Sun

  8. Harbin Univ. of Science and Technology , Harbin, China

    Xianhua Song

  9. Harbin Engineering University , Harbin, China

    Hongtao Song

  10. Harbin Sea of Clouds & Computer Tech. , Harbin, China

    Zeguang Lu

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© 2016 Springer Science+Business Media Singapore

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Huang, W., Ma, J., Zhang, E. (2016). Using Gaussian Mixture Model to Fix Errors in SFS Approach Based on Propagation. In: Che, W., et al. Social Computing. ICYCSEE 2016. Communications in Computer and Information Science, vol 623. Springer, Singapore. https://doi.org/10.1007/978-981-10-2053-7_63

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  • DOI: https://doi.org/10.1007/978-981-10-2053-7_63

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2052-0

  • Online ISBN: 978-981-10-2053-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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