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Video tracking and behaviour segmentation of laboratory rodents

  • E. Lomakina-Rumyantseva
  • P. Voronin
  • D. Kropotov
  • D. Vetrov
  • A. Konushin
Representation, Processing, Analysis and Understanding of Images

Abstract

In this paper a system for laboratory rodent video tracking and behavior segmentation is proposed. A new real-time mouse pose estimation method is proposed based on semi-automatically generated animal shape model. Behavior segmentation into separate behavior acts is considered as a signal segmentation problem using hidden Markov models (HMM). Conventional first order HMM supposes a geometric prior distribution on segment’s length, which is inadequate for behavior segmentation. We propose a modification of conventional first order HMM that allows any prior distribution on segment’s length. Experiments show that the developed approach can lead to more adequate results comparing to conventional HMM.

Keywords

Hidden Markov Models video tracking behaviour analysis active shape models Graph cuts minimal description length 

References

  1. 1.
    E. S. Lein et al., “Genome-Wide Atlas of Gene Expression in the Adult Mouse Brain,” Nature 445, 168–176 (2007).CrossRefGoogle Scholar
  2. 2.
    B. Spruijt and L. DeVisser, “Advanced Behavioral Screening: Automated Home Cage Ethology,” Drug Discovery Today: Technologies 3(2), 231–237 (2006).CrossRefGoogle Scholar
  3. 3.
    Ethovision, Available from http://www.noldus.com.
  4. 4.
    Any-Maze, Available from http://www.anymaze.com.
  5. 5.
    N. Kafkafi, et al., “Darting Behavior: a Quantative Movement Pattern Designed for Discrimination and Replicability in Mouse Locomotor Behavior,” Behavioral Brain Research 142, 193–205 (2003).CrossRefGoogle Scholar
  6. 6.
    N. Kafkafi, and G. I. Elmer, “Activity Density in the Open Field: A Measure for Differentiating the Effect of Psychostimulants,” Pharmacology, Biochemistry, and Behavior 80, 239–249 (2005).CrossRefGoogle Scholar
  7. 7.
    A. Konushin, D. Vetrov, D. Kropotov, P. Voronin, M. Sidneev, E. Lomakina-Rumyatseva, I. Zarayskaya, and K. Anokhin, “Behavior Video Tracking System with Automatic Segmentation into Behavioral Acts,” in Proc. of GraphiCon (Moscow, 2008).Google Scholar
  8. 8.
    R. J. Elliot, L. Aggoun, and J. B. Moore, Hidden Markov Models: Estimation and Control (Springer, 1995).Google Scholar
  9. 9.
    T. Cootes, “An Introduction to Active Shape Models. Model Based Methods in Analysis of Biochemical Images” (2000), pp. 223–248.Google Scholar
  10. 10.
    C. Twining, et al., “Robust Tracking and Posture Description for Laboratory Rodents Using Active Shape Models,” Behavior Research Methods, Instruments and Computers, Measuring Behavior (Special Issue, 2001), Vol. 33, No. 3.Google Scholar
  11. 11.
    V. Vezhnevets and V. Konouchine, ““Grow-Cut”-Interactive Multi-Label N-D Image Segmentation,” in Proc. of GraphiCon (Novosibirsk, 2005).Google Scholar
  12. 12.
    Y. Li, et al., “Video Object Cut and Paste,” in Proc. of ACM SIGGRAPH (Los Angeles, 2005), pp. 595–600.Google Scholar
  13. 13.
    T. Cootes, Timeline of Developments in Algorithms for Finding Correspondences across Sets of Shapes and Images (2005).Google Scholar
  14. 14.
    R. H. Davies et al., “A Minimum Description Length Approach to Statistical Shape Modeling,” IEEE Transactions on Medical Imaging 21, 525–537 (2002).CrossRefGoogle Scholar
  15. 15.
    A. Ericsson, “Automatic Shape Modeling with Applications in Medical Imaging,” PhD Thesis (Lund University, Centre for Mathematical Sciences, 2006).Google Scholar
  16. 16.
    H. H. Thodberg and H. Olafsdottir, “Adding Curvature to Minimum Description Length Shape Models,” in Proc. British Machine Vision Conf. (Norwich, 2003).Google Scholar
  17. 17.
    A. Ericsson and J. Karlsson, “Measures for Benchmarking of Automatic Correspondence Algorithms,” Journal of Mathematical Imaging and Vision 28(3), 225–241 (2007).CrossRefMathSciNetGoogle Scholar
  18. 18.
    A. Dempster, N. Laird, and D. Rubin, “Maximum Likelihood from Incomplete Data Via the Em Algorithm,” Journal of the Royal Statistical Society 39(1), 1–38 (1977).zbMATHMathSciNetGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2009

Authors and Affiliations

  • E. Lomakina-Rumyantseva
    • 1
  • P. Voronin
    • 1
  • D. Kropotov
    • 2
  • D. Vetrov
    • 1
  • A. Konushin
    • 1
  1. 1.Faculty of Computational Mathematics and CyberneticsMoscow State University, Computational Mathematics and Cybernetics DepartmentMoscowRussia
  2. 2.Dorodnicyn Computing CentreRussian Academy of ScienceMoscowRussia

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