Abstract
Image sequence processing techniques are used to study exchange, growth, and transport processes and to tackle key questions in environmental physics and biology. These applications require high accuracy for the estimation of the motion field since the most interesting parameters of the dynamical processes studied are contained in first-order derivatives of the motion field or in dynamical changes of the moving objects. Therefore the performance and optimization of low-level motion estimators is discussed. A tensor method tuned with carefully optimized derivative filters yields reliable and dense displacement vector fields (DVF) with an accuracy of up to a few hundredth pixels/frame for real-world images. The accuracy of the tensor method is verified with computer-generated sequences and a calibrated image sequence. With the improvements in accuracy the motion estimation is now rather limited by imperfections in the CCD sensors, especially the spatial nonuniformity in the responsivity. With a simple two-point calibration, these effects can efficiently be suppressed. The application of the techniques to the analysis of plant growth, to ocean surface microturbulence in IR image sequences, and to sediment transport is demonstrated.
Chapter PDF
Keywords
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.
References
S. S. Beauchemin and J. L. Barron, 1997. The computation of optical flow, ACM Computing Surveys, 27(3), pp. 433–467.
J. Bigün and G. Granlund, 1987. Optimal orientation detection of linear symmetry. Proc. First Intern. Conf. on Comp. Vision, ICCV'87, London, June 8–11, 1987, pp. 433–438.
C. H. Chu and E. J. Delp, 1989. Estimating displacement vectors from an image sequence, J. Opt. Soc. Am. A6(6), pp. 871–878.
H. Farid and E.P. Simoncelli, 1997. Optimally rotation-equivariant directional derivative kernels. 7th Int. Conf. Computer Analysis of Images and Patterns, Kiel, pp. 207–214.
H. Hau\ecker, 1995. Mehrgitter-Bewegungssegmentierung in Bildfolgen mit Anwendung zur Detektion von Sedimentverlagerungen, Diploma thesis, Univ. Heidelberg.
H. Hau\ecker and B. JÄhne, 1997. A tensor approach for precise computation of dense displacement vector fields, Proc. Mustererkennung 1997, Braunschweig, 15–17. September 1997, E. Paulus und F. M. Wahl (Hrsg.), Informatik Aktuell, Springer, Berlin, pp. 199–208.
B. JÄhne, 1993. Spatio-Temporal Image Processing, Theory and Scientific Applications, Lecture Notes in Computer Science, Vol. 751, Springer, Berlin, 1993.
B. JÄhne, 1997. Practical Handbook on Digital Image Processing for Scientific Applications, CRC-Press, Boca Raton, FL, USA.
B. JÄhne, 1997. Performance characteristics of low-level motion estimators in spatiotemporal images, Proc. DAGM-Workshop Performance Characteristics and Quality of Computer Vision Algorithms, Braunschweig, September 18, 1997, W. Foerstner (ed.), Institute of Photogrammetry, Univ. Bonn.
B. JÄhne, 1997. SIMD-Bildverarbeitungsalgorithmen mit dem Multimedia Extension-Instruktionssatz (MMX) von Intel, Automatisierungstechnik AT, 10, pp. 453–460.
B. JÄhne and H. Hau\ecker, 1998. Air-Water Gas Exchange, Annual Rev. Fluid Mech., 30, pp. 443–468.
M. Kass, and A. Witkin, 1987. Analyzing Oriented Patterns, Comp. Vision Graphics Image Proc., 37, pp. 362–385.
H. Knutsson, 1989. Representing local structure using tensors, 6th Scandinavian Conf. Image Analysis, Oulu, Finland, pp. 244–251.
P. S. Liss and R. A. Duce (eds.). The Sea Surface and Global Change, Cambridge Univ. Press, Cambridge, UK, 1997.
B. D. Lucas, and T. Kanade. An iterative image-registration technique with an application to stereo vision, Proc. DARPA Image Understanding Workshop, pp. 121–30.
M. Otte, and H.-H. Nagel, 1994. Optical flow estimation: advances and comparisons, Proc. ECCV'94, Vol. II, J. O. Eklundh (ed.), Springer, Berlin, pp. 51–60.
W. H. Press, S. A. Teukolsky, W. T. Vetterling, B. P.:Flannery, 1992. Numerical recipes in C: The Art of Scientific Computing, Cambridge Univ. Press
A. R. Rao and B. G. Schunck, 1989. Computing oriented texture fields, Proceedings CVPR'89, San Diego, CA, pp. 61–68, IEEE Computer Society Press, Los Alamitos.
A. R. Rao, 1990. A Taxonomy for Texture Description and Identification, Springer, New York
H. Scharr, S. Körkel, and B. JÄhne, 1997. Numerische Isotropieoptimierung von FIR-Filtern mittels QuerglÄttung, Proc. Mustererkennung 1997, Braunschweig, 15–17. September 1997, E. Paulus und F. M. Wahl (Hrsg.), Informatik Aktuell, Springer, Berlin, pp. 367–374.
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 1998 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Jähne, B., Haußecker, H., Scharr, H., Spies, H., Schmundt, D., Schurr, U. (1998). Study of dynamical processes with tensor-based spatiotemporal image processing techniques. In: Burkhardt, H., Neumann, B. (eds) Computer Vision — ECCV’98. ECCV 1998. Lecture Notes in Computer Science, vol 1407. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0054750
Download citation
DOI: https://doi.org/10.1007/BFb0054750
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-64613-6
Online ISBN: 978-3-540-69235-5
eBook Packages: Springer Book Archive