Abstract
We introduce the concept of complementarity between data and smoothness term in modern variational optic flow methods. First we design a sophisticated data term that incorporates HSV colour representation with higher order constancy assumptions, completely separate robust penalisation, and constraint normalisation. Our anisotropic smoothness term reduces smoothing in the data constraint direction instead of the image edge direction, while enforcing a strong filling-in effect orthogonal to it. This allows optimal complementarity between both terms and avoids undesirable interference. The high quality of our complementary optic flow (COF) approach is demonstrated by the current top ranking result at the Middlebury benchmark.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Horn, B., Schunck, B.: Determining optical flow. Artificial Intelligence 17, 185–203 (1981)
Nagel, H.H., Enkelmann, W.: An investigation of smoothness constraints for the estimation of displacement vector fields from image sequences. IEEE Transactions on Pattern Analysis and Machine Intelligence 8, 565–593 (1986)
Black, M.J., Anandan, P.: Robust dynamic motion estimation over time. In: Proc. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Maui, HI, pp. 292–302. IEEE Computer Society Press, Los Alamitos (1991)
Brox, T., Bruhn, A., Papenberg, N., Weickert, J.: High accuracy optical flow estimation based on a theory for warping. In: Pajdla, T., Matas, J(G.) (eds.) ECCV 2004. LNCS, vol. 3024, pp. 25–36. Springer, Heidelberg (2004)
Schnörr, C.: Segmentation of visual motion by minimizing convex non-quadratic functionals. In: Proc. Twelfth International Conference on Pattern Recognition, Jerusalem, Israel, vol. A, pp. 661–663. IEEE Computer Society Press, Los Alamitos (1994)
Tretiak, O., Pastor, L.: Velocity estimation from image sequences with second order differential operators. In: Proc. Seventh International Conference on Pattern Recognition, Montreal, Canada, July 1984, pp. 16–19 (1984)
Bruhn, A., Weickert, J.: Towards ultimate motion estimation: Combining highest accuracy with real-time performance. In: Proc. Tenth International Conference on Computer Vision, Beijing, China, vol. 1, pp. 749–755. IEEE Computer Society Press, Los Alamitos (2005)
van de Weijer, J., Gevers, T.: Robust optical flow from photometric invariants. In: Proc. 2004 IEEE International Conference on Image Processing, Singapore, vol. 3, pp. 1835–1838. IEEE Signal Processing Society (October 2004)
Mileva, Y., Bruhn, A., Weickert, J.: Illumination-robust variational optical flow with photometric invariants. In: Hamprecht, F.A., Schnörr, C., Jähne, B. (eds.) DAGM 2007. LNCS, vol. 4713, pp. 152–162. Springer, Heidelberg (2007)
Simoncelli, E.P., Adelson, E.H., Heeger, D.J.: Probability distributions of optical flow. In: Proc. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Maui, HI, pp. 310–315. IEEE Computer Society Press, Los Alamitos (1991)
Lai, S.H., Vemuri, B.C.: Reliable and efficient computation of optical flow. International Journal of Computer Vision 29(2), 87–105 (1998)
Alvarez, L., Esclarín, J., Lefébure, M., Sánchez, J.: A PDE model for computing the optical flow. In: Proc. XVI Congreso de Ecuaciones Diferenciales y Aplicaciones, Las Palmas de Gran Canaria, Spain, September 1999, pp. 1349–1356 (1999)
Weickert, J., Schnörr, C.: A theoretical framework for convex regularizers in PDE-based computation of image motion. International Journal of Computer Vision 45(3), 245–264 (2001)
Sun, D., Roth, S., Lewis, J., Black, M.: Learning optical flow. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part III. LNCS, vol. 5304, pp. 83–97. Springer, Heidelberg (2008)
Roth, S., Black, M.: Steerable random fields. In: Proc. 2007 IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil. IEEE Computer Society Press, Los Alamitos (2007)
Bruhn, A., Weickert, J., Kohlberger, T., Schnörr, C.: A multigrid platform for real-time motion computation with discontinuity-preserving variational methods. International Journal of Computer Vision 70(3), 257–277 (2006)
Golland, P., Bruckstein, A.M.: Motion from color. Computer Vision and Image Understanding 68(3), 346–362 (1997)
Förstner, W., Gülch, E.: A fast operator for detection and precise location of distinct points, corners and centres of circular features. In: Proc. ISPRS Intercommission Conference on Fast Processing of Photogrammetric Data, Interlaken, Switzerland, June 1987, pp. 281–305 (1987)
Black, M.J., Anandan, P.: The robust estimation of multiple motions: parametric and piecewise smooth flow fields. Computer Vision and Image Understanding 63(1), 75–104 (1996)
Perona, P., Malik, J.: Scale space and edge detection using anisotropic diffusion. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 629–639 (1990)
Weickert, J.: Theoretical foundations of anisotropic diffusion in image processing. Computing Supplement 11, 221–236 (1996)
Baker, S., Roth, S., Scharstein, D., Black, M., Lewis, J., Szeliski, R.: A database and evaluation methodology for optical flow. In: Proc. 2007 IEEE International Conference on Computer Vision, Rio de Janeiro, Brazil. IEEE Computer Society Press, Los Alamitos (2007)
Barron, J.L., Fleet, D.J., Beauchemin, S.S.: Performance of optical flow techniques. International Journal of Computer Vision 12(1), 43–77 (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zimmer, H. et al. (2009). Complementary Optic Flow. In: Cremers, D., Boykov, Y., Blake, A., Schmidt, F.R. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2009. Lecture Notes in Computer Science, vol 5681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03641-5_16
Download citation
DOI: https://doi.org/10.1007/978-3-642-03641-5_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03640-8
Online ISBN: 978-3-642-03641-5
eBook Packages: Computer ScienceComputer Science (R0)