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2D Shape Measurement of Multiple Moving Objects by GMM Background Modeling and Optical Flow

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Image Analysis and Recognition (ICIAR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3656))

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Abstract

In mineral processing industry, it is often useful to be able to obtain statistical information about the size distribution of ore fragments that move relatively to a static but noisy background. In this paper, we introduce a novel approach to estimate the 2D shapes of multiple moving objects in noisy background. Our approach combines adaptive Gaussian mixture model (GMM) for background subtraction and optical flow methods supported by temporal differencing in order to achieve robust and accurate extraction of the shapes of moving objects. The algorithm works well for image sequences having many moving objects with different sizes as demonstrated by experimental results on real image sequences.

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Author information

Authors and Affiliations

  1. CIMS, Computing Science Dept., University of Alberta, Alberta, T6G-2E8, Canada

    Dongxiang Zhou & Hong Zhang

  2. JCISS, School of Electronic Science and Engineering, National University of Defense Technology, ChangSha, 410073, China

    Dongxiang Zhou

Authors
  1. Dongxiang Zhou
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  2. Hong Zhang
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Editor information

Editors and Affiliations

  1. Electrical and Computer Engineering Department, University of Waterloo,  

    Mohamed Kamel

  2. FEUP - Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, P.O. Box, 4200-465, Porto, Portugal

    Aurélio Campilho

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© 2005 Springer-Verlag Berlin Heidelberg

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Zhou, D., Zhang, H. (2005). 2D Shape Measurement of Multiple Moving Objects by GMM Background Modeling and Optical Flow. In: Kamel, M., Campilho, A. (eds) Image Analysis and Recognition. ICIAR 2005. Lecture Notes in Computer Science, vol 3656. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11559573_96

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  • DOI: https://doi.org/10.1007/11559573_96

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-29069-8

  • Online ISBN: 978-3-540-31938-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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