Interacting Adaptive Filters for Multiple Objects Detection

Part of the Computational Imaging and Vision book series (CIVI, volume 41)

Abstract

In this chapter, we consider a marked point process framework for analyzing high resolution images, which can be interpreted as an extension of the Markov random field modelling (see Chaps. 14 and 15). The targeted applications concern object detection. Similarly to Chap. 10, we assume that the information embedded in the image consists of a configuration of objects rather than a set of pixels. We focus on a collection of objects having similar shapes in the image. We define a model applied in a configuration space consisting of an unknown number of parametric objects. A density, composed of a prior and a data term, is described. The prior contains information on the object shape and relative position in the image. The data term is constructed from local filters matching the object shape. Two algorithms for optimizing such a model are described. Finally, two applications, concerning counting of a given population, are detailed. The first application concerns small lesions in the brain whereas the second aims at counting individuals in a flamingo colony.

References

  1. 18.
    Baddeley, A., van Lieshout, M.N.M.: Stochastic geometry models in high-level vision. Stat. Images 1, 231–254 (1993) Google Scholar
  2. 33.
    Besag, J.: Spatial interaction and the statistical analysis of lattice systems (with discussion). J. R. Stat. Soc. B 36(2), 192–236 (1974) MATHMathSciNetGoogle Scholar
  3. 86.
    Cross, A., Jain, K.: Markov random field texture models. IEEE Trans. Pattern Anal. Mach. Intell. 5(1), 25–39 (1983) CrossRefGoogle Scholar
  4. 89.
    Daley, D.J., Vere-Jones, D.: An Introduction to the Theory of Point Processes: Elementary Theory and Methods. Probability and Its Applications, vol. I. Springer, Berlin (2003) MATHGoogle Scholar
  5. 90.
    Daley, D.J., Vere-Jones, D.: An Introduction to the Theory of Point Processes: General Theory and Structure. Probability and Its Applications, vol. II. Springer, Berlin (2008) CrossRefMATHGoogle Scholar
  6. 100.
    Descamps, S., Descombes, X., Béchet, A., Zerubia, J.: Automatic flamingo detection using a multiple birth and death process. In: Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP, pp. 1113–1116 (2008) Google Scholar
  7. 101.
    Descombes, X., Kruggel, F., Wollny, G., Gertz, H.J.: An object based approach for detecting small brain lesions: application to Virchow-Robin spaces. IEEE Trans. Med. Imaging 23(2), 246–255 (2004) CrossRefGoogle Scholar
  8. 102.
    Descombes, X., Minlos, R., Zhizhina, E.: Object extraction using a stochastic birth-and-death dynamics in continuum. J. Math. Imaging Vis. 33(3), 347–359 (2009) CrossRefMathSciNetGoogle Scholar
  9. 183.
    Geman, S., Geman, D.: Stochastic relaxation, Gibbs distribution, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6(6), 711–741 (1984) CrossRefGoogle Scholar
  10. 185.
    Geyer, C.J., Møller, J.: Simulation and likelihood inference for spatial point processes. Scand. J. Stat. 21(4), 359–373 (1994) MATHGoogle Scholar
  11. 190.
    Green, P.: Reversible jump MCMC computation and Bayesian model determination. Biometrika 82(4), 711–732 (1995) CrossRefMATHMathSciNetGoogle Scholar
  12. 220.
    Heier, L.A., Bauer, C.J., Schwartz, L., Zimmerman, R.D., Morgelli, S., Deck, M.D.: Large Virchow-Robin spaces: MR-clinical correlation. Am. J. Neuroradiol. 10(5), 929–936 (1989) Google Scholar
  13. 239.
    Johnson, A.R., Cézilly, F.: The Greater Flamingo. T & AD Poyse (2007) Google Scholar
  14. 269.
    Lacoste, C., Descombes, X., Zerubia, J.: Point processes for unsupervised line network extraction in remote sensing. IEEE Trans. Pattern Anal. Mach. Intell. 27(1), 1568–1579 (2005) CrossRefGoogle Scholar
  15. 270.
    Lafarge, F., Descombes, X., Zerubia, J., Pierrot-Deseilligny, M.: Automatic building extraction from DEMs using an object approach and application to the 3D-city modeling. J. Photogramm. Remote Sens. 63(3), 365–381 (2008) CrossRefGoogle Scholar
  16. 323.
    Ortner, M., Descombes, X., Zerubia, J.: Building outline extraction from digital elevation models using marked point processes. Int. J. Comput. Vis. 72(2), 107–132 (2007) CrossRefGoogle Scholar
  17. 335.
    Perrin, G., Descombes, X., Zerubia, J.: 2D and 3D vegetation resource parameters assessment using marked point processes. In: Tang, Y.Y., Wang, S.P., Yeung, D.S., Yan, H., Lorette, G. (eds.) Proceedings of the 18th International Conference on Pattern Recognition, Hong Kong, China, August 2006, vol. 1, pp. 1–4. IEEE Computer Society, Los Alamitos (2006) Google Scholar
  18. 349.
    Preston, C.: Spatial birth-and-death processes. Bull. Int. Stat. Inst. 46(2), 371–391 (1977) MathSciNetGoogle Scholar
  19. 365.
    Rue, H., Hurn, M.: Bayesian object identification. Biometrika 86(3), 649–660 (1999) CrossRefMATHMathSciNetGoogle Scholar
  20. 373.
    Scheltens, P., Erkinjunti, T., Leys, D., Wahlund, L.O., del Ser, T., Pasquier, F., Barkhof, F., Mantyla, R., Bowler, J., Wallin, A., Ghika, J., Fazekas, F., Pantoni, L.: White matter changes on CT and MRI: an overview of visual rating scales. Eur. J. Neurol. 39(2), 80–89 (1998) CrossRefGoogle Scholar
  21. 393.
    Stoica, R., Descombes, X., van Lieshout, M.N.M., Zerubia, J.: An application of marked point processes to the extraction of linear networks for images. In: Mateu, J., Montes, F. (eds.) Spatial Statitics Through Applications, pp. 289–314. WIT Press, Southampton (2002) Google Scholar
  22. 418.
    Tupin, F., Maitre, H., Mangin, J.-M., Nicolas, J.-M., Pechersky, E.: Detection of linear features in SAR images: application to the road network extraction. IEEE Trans. Geosci. Remote Sens. 36(2), 434–453 (1998) CrossRefGoogle Scholar
  23. 423.
    van Lieshout, M.N.M.: Markov Point Processes and Their Applications. Imperial College Press, London (2000) CrossRefMATHGoogle Scholar

Copyright information

© Springer-Verlag London Limited 2012

Authors and Affiliations

  1. 1.Laboratoire d’Informatique, Signaux et Systèmes de Sophia-Antipolis I3SUMR6070, UNS CNRS 2000Sophia Antipolis CedexFrance

Personalised recommendations