Shape Normalizing and Tracking Dancing Worms

  • Carmine Sansone
  • Daniel Pucher
  • Nicole M. Artner
  • Walter G. Kropatsch
  • Alessia Saggese
  • Mario Vento
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10029)

Abstract

During spawning, the marine worms Platynereis dumerilii exhibit certain swimming behaviors, which are described as nuptial dance. To address the hypothesis that characteristic male and female spawning behaviors are required for successful spawning and fertilization, we propose a 2D tracking approach enabling the extraction of spatio-temporal data to quantify gender-specific behaviors. One of the main issues is the complex interaction between the worms leading to collisions, occlusions, and interruptions of their continuous trajectories. To maintain the individual identities under these challenging interactions a combined tracking and re-identification approach is proposed. The re-identification is based on a set of features, which take into account position, shape and appearance of the worms. These features include the normalized shape of a worm, which is computed using a novel approach based on its distance transform and skeleton.

Keywords

Object tracking Appearance models Shape normalization Shape analysis 

References

  1. 1.
    Aigerman, N., Poranne, R., Lipman, Y.: Lifted bijections for low distortion surface mappings. ACM Trans. Graph 33(4), 69:1–69:12 (2014)CrossRefGoogle Scholar
  2. 2.
    Baek, J.H., Cosman, P., Feng, Z., Silver, J., Baek, J., Schafer, W.: Using machine vision to analyze and classify Caenorhabditis elegans behavioral phenotypes quantitatively. J. Neurosci. Methods 118, 9–21 (2002)CrossRefGoogle Scholar
  3. 3.
    Bentley, M.G., Olive, P.J.W., Last, K.: Sexual satellites, moonlight and the nuptial dances of worms: the influence of the moon on the reproduction of marine animals. Earth, Moon, Planets 85, 67–84 (1999)CrossRefGoogle Scholar
  4. 4.
    Chai, T., Draxler, R.R.: Root mean square error (RMSE) or mean absolute error (MAE)? Arguments against avoiding rmse in the literature. Geosci. Model Dev. 7(3), 1247–1250 (2014)CrossRefGoogle Scholar
  5. 5.
    Coifman, B., Beymer, D., McLauchlan, P., Malik, J.: A real-time computer vision system for vehicle tracking and traffic surveillance. Transp. Res. Part C: Emerg. Technol. 6(4), 271–288 (1998)CrossRefGoogle Scholar
  6. 6.
    Hardaker, L.A., Singer, E., Kerr, R., Schafer, W.R.: Serotonin modulates locomotory behavior and coordinates egg-laying and movement in C. elegans. J. Neurobiol. 49, 303–313 (2001)CrossRefGoogle Scholar
  7. 7.
    Hoshi, K., Shingai, R.: Computer-driven automatic identification of locomotion states in Caenorhabditis elegans. J. Neurosci. Methods 157(2), 355–363 (2006)CrossRefGoogle Scholar
  8. 8.
    Huang, K.M.: Tracking and Analysis of Caenorhabditis Elegans Behavior Using Machine Vision. ProQuest, Ann Arbor (2008)Google Scholar
  9. 9.
    Husson, S.J., Costa, W.S., Schmitt, C., Gottschalk, A.: Keeping track of worm trackers. In: WormBook (2012)Google Scholar
  10. 10.
    Kovvali, N., Banavar, M.K., Spanias, A.: An Introduction to Kalman Filtering with MATLAB Examples. Synthesis Lectures on Signal Processing. Morgan and Claypool Publishers, San Rafael (2013)MATHGoogle Scholar
  11. 11.
    Lascio, R.D., Foggia, P., Percannella, G., Saggese, A., Vento, M.: A real time algorithm for people tracking using contextual reasoning. CVIU 117(8), 892–908 (2013)Google Scholar
  12. 12.
    Osborne, J.W.: Best Practices in Quantitative Methods. Sage Publications, Thousand Oaks (2008). Ed. by Osborne, J.W.: Includes bibliographical references and indexCrossRefGoogle Scholar
  13. 13.
    Pucher, D.: 2D tracking of platynereis dumerilii worms during spawning. Technical report PRIP-TR-135, TU Wien, Austria, April 2016Google Scholar
  14. 14.
    Ramot, D., Johnson, B.E., Berry, T.L., Carnell, L., Goodman, M.B.: The parallel worm tracker: a platform for measuring average speed and drug-induced paralysis in nematodes. PLoS ONE 3(5), e2208+ (2008)CrossRefGoogle Scholar
  15. 15.
    Shingai, R.: Durations and frequencies of free locomotion in wild type and gabaergic mutants of Caenorhabditis elegans. Neurosci. Res. 38(1), 71–84 (2000)CrossRefGoogle Scholar
  16. 16.
    Swierczek, N.A., Giles, A.C., Rankin, C.H., Kerr, R.A.: High-throughput behavioral analysis in C. elegans. Nat. Methods 8(7), 592–598 (2011)CrossRefGoogle Scholar
  17. 17.
    Wang, S.J., Wang, Z.-W.: Track a worm, an open-source system for quantitative assessment of C. elegans locomotory, bending behavior. PLoS ONE 8(7), 1–10 (2013)Google Scholar
  18. 18.
    Yemini, E.: High-Throughput, Single-worm Tracking and Analysis in Caenorhabditis Elegans. University of Cambridge, Cambridge (2013)Google Scholar
  19. 19.
    Zantke, J., Bannister, S., Rajan, V.B.V., Raible, F., Tessmar-Raible, K.: Genetic, genomic tools for the marine annelid platynereis dumerilii. Genetics 197, 19–31 (2014). World Polychaeta Database (WPolyDb)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Carmine Sansone
    • 1
    • 2
  • Daniel Pucher
    • 1
  • Nicole M. Artner
    • 1
  • Walter G. Kropatsch
    • 1
  • Alessia Saggese
    • 2
  • Mario Vento
    • 2
  1. 1.Pattern Recognition and Image Processing GroupTU WienViennaAustria
  2. 2.Department of Information Engineering, Electrical Engineering and Applied Mathematics (DIEM), Faculty of EngineeringUniversity of SalernoFiscianoItaly

Personalised recommendations