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A Multiple Model Probability Hypothesis Density Tracker for Time-Lapse Cell Microscopy Sequences

  • Seyed Hamid Rezatofighi
  • Stephen Gould
  • Ba-Ngu Vo
  • Katarina Mele
  • William E. Hughes
  • Richard Hartley
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7917)

Abstract

Quantitative analysis of the dynamics of tiny cellular and subcellular structures in time-lapse cell microscopy sequences requires the development of a reliable multi-target tracking method capable of tracking numerous similar targets in the presence of high levels of noise, high target density, maneuvering motion patterns and intricate interactions. The linear Gaussian jump Markov system probability hypothesis density (LGJMS-PHD) filter is a recent Bayesian tracking filter that is well-suited for this task. However, the existing recursion equations for this filter do not consider a state-dependent transition probability matrix. As required in many biological applications, we propose a new closed-form recursion that incorporates this assumption and introduce a general framework for particle tracking using the proposed filter. We apply our scheme to multi-target tracking in total internal reflection fluorescence microscopy (TIRFM) sequences and evaluate the performance of our filter against the existing LGJMS-PHD and IMM-JPDA filters.

Keywords

Gaussian Mixture Model Particle Tracking Jump Markov System Multiple Object Tracking Probabilistic Hypothesis Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Seyed Hamid Rezatofighi
    • 1
    • 2
  • Stephen Gould
    • 1
  • Ba-Ngu Vo
    • 3
  • Katarina Mele
    • 2
  • William E. Hughes
    • 4
    • 5
  • Richard Hartley
    • 1
    • 6
  1. 1.College of Engineering & Computer Sci.Australian National UniversityAustralia
  2. 2.CSIRO Math., Informatics & StatisticsQuantitative Imaging GroupAustralia
  3. 3.Department of Electrical and Computer EngineeringCurtin UniversityAustralia
  4. 4.The Garvan Institute of Medical ResearchAustralia
  5. 5.Department of MedicineSt. Vincent’s HospitalAustralia
  6. 6.National ICT (NICTA)Australia

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