Regularised Range Flow

  • Hagen Spies
  • Bernd Jähne
  • John L. Barron
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1843)


Extending a differential total least squares method for range flow estimation we present an iterative regularisation approach to compute dense range flow fields. We demonstrate how this algorithm can be used to detect motion discontinuities. This can can be used to segment the data into independently moving regions. The different types of aperture problem encountered are discussed. Our regularisation scheme then takes the various types of flow vectors and combines them into a smooth flow field within the previously segmented regions. A quantitative performance analysis is presented on both synthetic and real data. The proposed algorithm is also applied to range data from castor oil plants obtained with the Biris laser range sensor to study the 3-D motion of plant leaves.


range flow range image sequences regularisation shape visual motion 


  1. 1.
    H. Spies, H. Haußecker, B. Jähne and J. L. Barron: Differential Range Flow Estimation. 21.Symposium für Mustererkennung DAGM’1999. Bonn (1999) 309–316Google Scholar
  2. 2.
    R. Szeliski: Estimating Motion from Sparse Range Data without Correspondence. ICCV’88. (1988) 207–216Google Scholar
  3. 3.
    B.K.P. Horn and J.G. Harris: Rigid Body Motion from Range Image Sequences. CVGIP: Image Understanding. 53(1) (1991) 1–13CrossRefzbMATHGoogle Scholar
  4. 4.
    B. Sabata and J.K. Aggarwal: Estimation of Motion from a Pair of Range Images: A Review. CVGIP: Image Understanding. 54(3) (1991) 309–324CrossRefzbMATHGoogle Scholar
  5. 5.
    L. Lucchese, G.M. Cortelazzo and A. Vettore: Estimating 3-D Roto-translations from Range Data by a Frequency Domain Technique. Conf. on Optical 3-D Measurement Techniques IV. Zürich (1997) 444–453Google Scholar
  6. 6.
    M. Harville, A. Rahimi, T. Darrell, G. Gordon and J. Woodfill: 3D Pose Tracking with Linear Depth and Brightness Constraints. ICCV’99, (1999) 206–213Google Scholar
  7. 7.
    B. Lucas and T. Kanade: An Iterative Image Registration Technique with an Application to Stereo Vision. Int. Joint Conf. on Artificial Intelligence. (1981) 674–679Google Scholar
  8. 8.
    H. Haußecker and H. Spies: Motion. In Handbook on Computer Vision and Applications, dEds.: B, Jähne, H. Haußecker and P. Geißler. Academic Press. (1999)Google Scholar
  9. 9.
    M. Yamamoto, P. Boulanger, J. Beraldin and M. Rioux: Direct Estimation of Range Flow on Deformable Shape from a Video Rate Range Camera. PAMI. 15(1) (1993) 82–89CrossRefGoogle Scholar
  10. 10.
    L.V Tsap, D.B. Goldgof and S. Sarkar: Model-Based Force-Driven Nonrigid Motion Recovery from Sequences of Range Images without Point Correspondences. Image and Vision Computing, 17(14) (1999) 997–1007CrossRefGoogle Scholar
  11. 11.
    S. Vedula, S. Baker, P. Rander, R. Collins and T. Kanade: Three-Dimensional Scene Flow. ICCV’99, (1999) 722–729Google Scholar
  12. 12.
    J. Gonzalez: Recovering Motion Parameters from a 2D Range Image Sequence. ICPR’96. (1996) 82–89Google Scholar
  13. 13.
    L. Zhao and C. Thorpe: Qualitative and Quantitative Car Tracking from a Range Image Sequence. CVPR’98, (1998) 496–501Google Scholar
  14. 14.
    J.-A. Beraldin, S.F. El-Hakim and F. Blais: Performance Evaluation of three Active Vision Systems Built at the National Research Council of Canada. Conf. on Optical 3-D Measurement Techniques III. Vienna (1995) 352–361Google Scholar
  15. 15.
    B. Jähne, H. Haußecker, H. Scharr, H. Spies, D. Schmundt and U. Schurr: Study of Dynamical Processes with Tensor-Based Spatiotemporal Image Processing Techniques. ECCV’ 98. (1998) 322–336Google Scholar
  16. 16.
    H. Haußecker, C. Garbe, H. Spies and B. Jähne: A Total Least Squares Framework for Low-Level Analysis of Dynamic Scenes and Processes. 21.Symposium für Mustererkennung DAGM’1999. (1999) 240–249Google Scholar
  17. 17.
    W. H. Press, S. A. Teukolsky, W.T. Vetterling and B.P. Flannery: Numerical Recipes in C: The Art of Scientific Computing. Cambridge University Press. (1992)Google Scholar
  18. 18.
    S. Van Huffel and J. Vandewalle: The Total Least Squares Problem: Computational Aspects and Analysis. Society for Industrial and Applied Mathematics. (1991)Google Scholar
  19. 19.
    Ch. Schnörr: Variational Methods for Adaptive Image Smoothing and Segmentation. In Handbook on Computer Vision and Applications, Eds.: B, Jähne, H. Haußecker and P. Geißler. Academic Press. (1999)Google Scholar
  20. 20.
    J. Weickert: On Discontinuity-Preserving Optic Flow. Proc. CVMR’ 98. (1998) 115–122Google Scholar
  21. 21.
    G. H. Golub and C. F. van Loan: Matrix Computation (3rd edition). The Johns Hopkins University Press. (1996)Google Scholar
  22. 22.
    B.K.P. Horn and B.G. Schunk: Determining Optical Flow. Artificial Intelligence 17 (1981) 185–204CrossRefGoogle Scholar
  23. 23.
    J.R. Bergen, p. Anandan, K.J. Hanna and R. Hingorani: Hierarchical Model-Based Motion Estimation. ECCV’92. (1992) 237–252Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Hagen Spies
    • 1
    • 2
  • Bernd Jähne
    • 1
  • John L. Barron
    • 2
  1. 1.Interdisciplinary Center for Scientific ComputingUniversity of HeidelbergHeidelbergGermany
  2. 2.Dept. of Comp. ScienceUniversity of Western OntarioLondonCanada

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