Efficient Monte Carlo Sampler for Detecting Parametric Objects in Large Scenes

  • Yannick Verdié
  • Florent Lafarge
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7574)


Point processes have demonstrated efficiency and competitiveness when addressing object recognition problems in vision. However, simulating these mathematical models is a difficult task, especially on large scenes. Existing samplers suffer from average performances in terms of computation time and stability. We propose a new sampling procedure based on a Monte Carlo formalism. Our algorithm exploits Markovian properties of point processes to perform the sampling in parallel. This procedure is embedded into a data-driven mechanism such that the points are non-uniformly distributed in the scene. The performances of the sampler are analyzed through a set of experiments on various object recognition problems from large scenes, and through comparisons to the existing algorithms.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Baddeley, A.J., Lieshout, M.V.: Stochastic geometry models in high-level vision. Journal of Applied Statistics 20 (1993)Google Scholar
  2. 2.
    Descombes, X., Minlos, R., Zhizhina, E.: Object extraction using a stochastic birth-and-death dynamics in continuum. JMIV 33 (2009)Google Scholar
  3. 3.
    Ge, W., Collins, R.: Marked point processes for crowd counting. In: CVPR, Miami, U.S. (2009)Google Scholar
  4. 4.
    Lafarge, F., Gimel’farb, G., Descombes, X.: Geometric feature extraction by a multi-marked point process. PAMI 32 (2010)Google Scholar
  5. 5.
    Lieshout, M.V.: Depth map calculation for a variable number of moving objects using markov sequential object processes. PAMI 30 (2008)Google Scholar
  6. 6.
    Mallet, C., Lafarge, F., Roux, M., Soergel, U., Bretar, F., Heipke, C.: A marked point process for modeling lidar waveforms. IP 19 (2010)Google Scholar
  7. 7.
    Lacoste, C., Descombe, X., Zerubia, J.: Point processes for unsupervised line network extraction in remote sensing. PAMI 27 (2005)Google Scholar
  8. 8.
    Sun, K., Sang, N., Zhang, T.: Marked Point Process for Vascular Tree Extraction on Angiogram. In: Yuille, A.L., Zhu, S.-C., Cremers, D., Wang, Y. (eds.) EMMCVPR 2007. LNCS, vol. 4679, pp. 467–478. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Utasi, A., Benedek, C.: A 3-D marked point process model for multi-view people detection. In: CVPR, Colorado Springs, U.S. (2011)Google Scholar
  10. 10.
    Green, P.: Reversible Jump Markov Chains Monte Carlo computation and Bayesian model determination. Biometrika 82 (1995)Google Scholar
  11. 11.
    Hastings, W.: Monte Carlo sampling using Markov chains and their applications. Biometrika 57 (1970)Google Scholar
  12. 12.
    Han, F., Tu, Z.W., Zhu, S.: Range image segmentation by an effective jump-diffusion method. PAMI 26 (2004)Google Scholar
  13. 13.
    Srivastava, A., Grenander, U., Jensen, G., Miller, M.: Jump-Diffusion Markov processes on orthogonal groups for object pose estimation. Journal of Statistical Planning and Inference 103 (2002)Google Scholar
  14. 14.
    Tu, Z., Zhu, S.: Image Segmentation by Data-Driven Markov Chain Monte Carlo. PAMI 24 (2002)Google Scholar
  15. 15.
    Harkness, M., Green, P.: Parallel chains, delayed rejection and reversible jump mcmc for object recognition. In: BMVC, Bristol, U.K (2000)Google Scholar
  16. 16.
    Byrd, J., Jarvis, S., Bhalerao, A.: On the parallelisation of mcmc-based image processing. In: IEEE International Symposium on Parallel and Distributed Processing, Atlanta, U.S. (2010)Google Scholar
  17. 17.
    Gonzalez, J., Low, Y., Gretton, A., Guestrin, C.: Parallel Gibbs sampling: From colored fields to thin junction trees. Journal of Machine Learning Research (2011)Google Scholar
  18. 18.
    Verdié, Y., Lafarge, F.: Towards the parallelization of Reversible Jump Markov Chain Monte Carlo algorithms for vision problems. Research report 8016, INRIA (2012)Google Scholar
  19. 19.
    Rochery, M., Jermyn, I., Zerubia, J.: Higher order active contours. IJCV 69 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yannick Verdié
    • 1
  • Florent Lafarge
    • 1
  1. 1.INRIA Sophia AntipolisFrance

Personalised recommendations