Skip to main content
SpringerLink
Log in

One-Shot Learned Priors in Augmented Active Appearance Models for Anatomical Landmark Tracking

  • Conference paper
  • First Online:
Computer Vision, Imaging and Computer Graphics – Theory and Applications (VISIGRAPP 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 983))

Abstract

In motion science, biology and robotics animal movement analyses are used for the detailed understanding of the human bipedal locomotion. For this investigations an immense amount of recorded image data has to be evaluated by biological experts. During this time-consuming evaluation single anatomical landmarks, for example bone ends, have to be located and annotated in each image. In this paper we show a reduction of this effort by automating the annotation with a minimum level of user interaction. Recent approaches, based on Active Appearance Models, are improved by priors based on anatomical knowledge and an online tracking method, requiring only a single labeled frame. In contrast, we propose a one-shot learned tracking-by-detection prior which overcomes the shortcomings of template drifts without increasing the number of training data. We evaluate our approach based on a variety of real-world X-ray locomotion datasets and show that our method outperforms recent state-of-the-art concepts for the task at hand.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Nyakatura, J.A., Andrada, E., Blickhan, R., Fischer, M.S.: Avian bipedal locomotion. In: 5th International Symposium on Adaptive Motion of Animals and Machines (AMAM). Elsevier (2011)

    Google Scholar 

  2. Andrada, E., Nyakatura, J.A., Bergmann, F., Blickhan, R.: Adjustments of global and local hindlimb properties during terrestrial locomotion of the common quail (coturnix coturnix). J. Exp. Biol. (2013)

    Google Scholar 

  3. Sigal, L., Balan, A.O., Black, M.J.: HumanEva: synchronized video and motion capture dataset and baseline algorithm for evaluation of articulated human motion. Int. J. Comput. Vis. 87(1–2), 4 (2010)

    Article  Google Scholar 

  4. Haase, D., Andrada, E., Nyakatura, J.A., Kilbourne, B.M., Denzler, J.: Automated approximation of center of mass position in X-ray sequences of animal locomotion. J. Biomech. 46, 2082–2086 (2013)

    Article  Google Scholar 

  5. Haase, D., Denzler, J.: 2D and 3D analysis of animal locomotion from biplanar X-ray videos using augmented active appearance models. EURASIP J. Image Video Process. (2013)

    Google Scholar 

  6. Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Trans. Pattern Anal. Mach. Intell. (2001)

    Google Scholar 

  7. Amthor, M., Haase, D., Denzler, J.: Fast and robust landmark tracking in X-ray locomotion sequences containing severe occlusions. In: International Workshop on Vision, Modelling, and Visualization (VMV). Eurographics Association (2012)

    Google Scholar 

  8. Mothes, O., Denzler, J.: Anatomical landmark tracking by one-shot learned priors for augmented active appearance models. In: International Conference on Computer Vision Theory and Applications (VISAPP), pp. 246–254 (2017)

    Google Scholar 

  9. Haase, D., Denzler, J.: Anatomical landmark tracking for the analysis of animal locomotion in X-ray videos using active appearance models. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 604–615. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21227-7_56

    Chapter  Google Scholar 

  10. Haase, D., Nyakatura, J.A., Denzler, J.: Multi-view active appearance models for the X-ray based analysis of avian bipedal locomotion. In: Mester, R., Felsberg, M. (eds.) DAGM 2011. LNCS, vol. 6835, pp. 11–20. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23123-0_2

    Chapter  Google Scholar 

  11. Lelieveldt, B., Üzümcü, M., van der Geest, R., Reiber, J., Sonka, M.: Multi-view active appearance models for consistent segmentation of multiple standard views: application to long- and short-axis cardiac MR images. In: International Congress Series (2003)

    Google Scholar 

  12. Amthor, M., Haase, D., Denzler, J.: Robust pictorial structures for X-ray animal skeleton tracking. In: International Conference on Computer Vision Theory and Applications (VISAPP). SCITEPRESS (2014)

    Google Scholar 

  13. Andriluka, M., Roth, S., Schiele, B.: Monocular 3D pose estimation and tracking by detection. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2010)

    Google Scholar 

  14. Li, L., Nawaz, T., Ferryman, J.: Pets 2015: datasets and challenge. In: 2015 12th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS). IEEE (2015)

    Google Scholar 

  15. Zhang, L., Li, Y., Nevatia, R.: Global data association for multi-object tracking using network flows. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2008. IEEE (2008)

    Google Scholar 

  16. Berclaz, J., Fleuret, F., Türetken, E., Fua, P.: Multiple object tracking using k-shortest paths optimization. IEEE Trans. Pattern Anal. Mach. Intell. 33, 1806–1819 (2011)

    Article  Google Scholar 

  17. Jiang, X., Haase, D., Körner, M., Bothe, W., Denzler, J.: Accurate 3D multi-marker tracking in X-ray cardiac sequences using a two-stage graph modeling approach. In: Wilson, R., Hancock, E., Bors, A., Smith, W. (eds.) CAIP 2013. LNCS, vol. 8048, pp. 117–125. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40246-3_15

    Chapter  Google Scholar 

  18. Dehghan, A., Modiri Assari, S., Shah, M.: GMMCP tracker: globally optimal generalized maximum multi clique problem for multiple object tracking. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2015)

    Google Scholar 

  19. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR 2005. IEEE (2005)

    Google Scholar 

  20. Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Trans. Pattern Anal. Mach. Intell. 32, 1627–1645 (2010)

    Article  Google Scholar 

  21. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)

    Article  Google Scholar 

  22. Breiman, L.: Classification and Regression Trees. CRC Press, Boca Raton (2017)

    Book  Google Scholar 

  23. Hariharan, B., Malik, J., Ramanan, D.: Discriminative decorrelation for clustering and classification. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, vol. 7575, pp. 459–472. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33765-9_33

    Chapter  Google Scholar 

  24. Sun, Y., Wang, X., Tang, X.: Deep convolutional network cascade for facial point detection. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE (2013)

    Google Scholar 

  25. Zhou, E., Fan, H., Cao, Z., Jiang, Y., Yin, Q.: Extensive facial landmark localization with coarse-to-fine convolutional network cascade. In: 2013 IEEE International Conference on Computer Vision Workshops (ICCVW). IEEE (2013)

    Google Scholar 

  26. Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0054760

    Chapter  Google Scholar 

  27. Kendall, D.G.: Shape manifolds, procrustean metrics, and complex projective spaces. Bull. London Math. Soc. 16, 81–121 (1984)

    Article  MathSciNet  Google Scholar 

  28. Jolliffe, I.: Principal Component Analysis. Springer Series in Statistics. Springer, Heidelberg (2002). https://doi.org/10.1007/b98835

    Book  MATH  Google Scholar 

  29. Berg, M., Cheong, O., Kreveld, M., Overmars, M.: Computational Geometry: Algorithms and Applications, 3rd edn. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-77974-2

    Book  MATH  Google Scholar 

  30. Baker, S., Matthews, I.: Lucas-kanade 20 years on: a unifying framework. Int. J. Comput. Vis. 56(3), 221–255 (2004)

    Article  Google Scholar 

  31. Freytag, A., Schadt, A., Denzler, J.: Interactive image retrieval for biodiversity research. In: Gall, J., Gehler, P., Leibe, B. (eds.) GCPR 2015. LNCS, vol. 9358, pp. 129–141. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24947-6_11

    Chapter  Google Scholar 

  32. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numer. Math. 1(1), 269–271 (1959)

    Article  MathSciNet  Google Scholar 

  33. Bellman, R.: On a routing problem. Q. Appl. Math. 16(1), 87–90 (1958)

    Article  Google Scholar 

Download references

Acknowledgments

The research was supported by grant DE 735/8-3 of the German Research Foundation (DFG).

Author information

Authors and Affiliations

  1. Computer Vision Group, Friedrich Schiller University Jena, 07737, Jena, Germany

    Oliver Mothes & Joachim Denzler

Authors
  1. Oliver Mothes
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Joachim Denzler
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Oliver Mothes .

Editor information

Editors and Affiliations

  1. University of Lisbon, Lisbon, Portugal

    Ana Paula Cláudio

  2. University of Strasbourg, Strasbourg, France

    Dominique Bechmann

  3. University of Angers, Angers, France

    Paul Richard

  4. Tokyo University of Science, Tokyo, Japan

    Takehiko Yamaguchi

  5. University of Münster, Münster, Nordrhein-Westfalen, Germany

    Lars Linsen

  6. University of Groningen, Groningen, The Netherlands

    Alexandru Telea

  7. Apple Inc., Cupertino, CA, USA

    Francisco Imai

  8. Jean Monnet University, Saint-Etienne, France

    Alain Tremeau

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Mothes, O., Denzler, J. (2019). One-Shot Learned Priors in Augmented Active Appearance Models for Anatomical Landmark Tracking. In: Cláudio, A., et al. Computer Vision, Imaging and Computer Graphics – Theory and Applications. VISIGRAPP 2017. Communications in Computer and Information Science, vol 983. Springer, Cham. https://doi.org/10.1007/978-3-030-12209-6_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-12209-6_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-12208-9

  • Online ISBN: 978-3-030-12209-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation