A new spatiotemporal approach for image analysis. Application to motion detection

  • Alice Caplier
  • Franck Luthon
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 970)


Image sequence analysis involves 3D data. Consequently, we propose a new spatiotemporal global approach for image sequence processing where an image sequence is regarded as a 3D data flow. This approach is illustrated in the case of motion detection in a Markovian framework. This leads to the development of a 3D Markov Random Field based algorithm which takes into account in the same way spatial and temporal dimensions. The required relaxation algorithm runs on (x,y,t) for the whole image sequence. Motion detection results illustrate the efficiency of this algorithm.


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  1. 1.
    S. Geman, D. Geman ”Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images”. In IEEE Trans. Pattern Anal. and Machine Intel. Vol. PAMI-6, N.6, November 1984, pp. 721–741.Google Scholar
  2. 2.
    G.R. Cross, A.K. Jain ”Markov Random Field Texture Models ”. In Trans. Pattern Anal. and Machine Intel. Vol. PAMI-5, N.1, January 1983, pp. 25–39.Google Scholar
  3. 3.
    P. Bouthemy, P. Lalande ”Recovery of moving object masks in image sequences using Markov Random Field modelling”. In Optical Engineering, Vol.32, N.6, June 1993, pp.1205–1212.Google Scholar
  4. 4.
    F. Luthon, A. Caplier ”Motion detection and segmentation in image sequences using Markov Random Field modelling”, in 4th Eurographics Animation and Simulation Workshop, Barcelona, Spain, pp. 265–275, September 1993.Google Scholar
  5. 5.
    D.W. Murray, B.F. Buxton ”Scene Segmentation from Visual Motion Using Global Optimization”. In IEEE Trans. Pattern Anal. and Machine Intel. Vol. PAMI-9, N.2, January 1987, pp. 220–228.Google Scholar
  6. 6.
    F. Heitz, P. Bouthemy ”Multimodal Motion Estimation and Segmentation Using Markov Random Fields”. In 10th Int. Conf. Pattern Recognition, Vol.1, Atlantic City, June 1990, pp. 378–383.Google Scholar
  7. 7.
    J. Besag ”On the Statistical Analysis of Dirty Pictures”. In Journal Royal Statistical Society, Vol. B-48, N.3, 1986, pp. 259–302.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1995

Authors and Affiliations

  • Alice Caplier
    • 1
  • Franck Luthon
    • 1
  1. 1.Image Processing and Pattern Recognition Laboratory Grenoble National Polytechnical InstituteLTIRF, INPGGrenoble CedexFrance

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