Optical flow using spatiotemporal filters
- David J. Heeger
- … show all 1 hide
Rent the article at a discountRent now
* Final gross prices may vary according to local VAT.Get Access
A model is presented, consonant with current views regarding the neurophysiology and psychophysics of motion perception, that combines the outputs of a set of spatiotemporal motion-energy filters to estimate image velocity. A parallel implementation computes a distributed representation of image velocity. A measure of image-flow uncertainty is formulated; preliminary results indicate that this uncertainty measure may be used to recognize ambiguity due to the aperture problem. The model appears to deal with the aperture problem as well as the human visual system since it extracts the correct velocity for some patterns that have large differences in contrast at different spatial orientations.
- S.T., Barnard and W.B., Thomson, “Disparity analysis of images,” IEEE Trans. Pami-2(4), pp. 333–340, 1980.
- B.K.P., Horn and B.G., Schunk, “Determining optical flow,” Artificial Intelligence, vol. 17; pp. 185–203, 1981.
- J.K., Kearney and W.B., Thompson, “An error analysis of gradient-based methods for optical flow estimation,” IEEE Trans. Pami-9(2), pp. 229–244, 1987.
- H., Gafni and Y., Zeevi, “A model for separation of spatial and temporal information in the visual system,” Biological Cybernetics, vol. 28; pp. 73–82, 1977.
- H., Gafni and Y., Zeevi, “A model for processing of movement in the visual system,” Biological Cybernetics, vol. 32; pp. 165–173, 1979.
- M., Fahle and T., Poggio, “Visual hyperacuity: Spatiotemporal interpolation in human vision,” Proc. R. Soc. (London), vol. 213; pp. 451–477, 1981.
- A.B. Watson and A.J. Ahumada, “A look at motion in the frequency domain,” Tech. Rep. 84352, NASA-Ames Research Center, 1983.
- A.B., Watson and A.J., Ahumada, “Model of human visual-motion sensing,” J. Opt. Soc. Amer. vol. A 2(2); pp. 322–342, 1985.
- E.H., Adelson and J.R., Bergen, “Spatiotemporal energy models for the perception of motion,” J. Opt. Soc. Amer., vol. A2(2), pp. 284–299, 1985.
- J.P.H.van, Santen and G., Sperling, “Elaborated reichardt detectors,” J. Opt. Soc. Amer., vol. A2(2); pp. 300–321, 1985.
- D.J. Fleet, The early processing of spatio-temporal visual information, Master's thesis, Dept. of Computer Science, Univ. of Toronto, 1984. (available as Tech. Report RBCVTR-84-7.)
- D.J. Fleet and A.D. Jepson, “A cascaded filter approach to the construction of velocity selective mechanisms,” Tech. Report RBCV-TR-84-6, Dept. of Computer Science, Univ. Toronto, 1984.
- E.C., Hildreth, “Computations underlying the measurement of visual motion,” Artificial Intelligence, vol. 23(3); pp. 309–355, 1984.
- D., Gabor, “Theory of communication,” J. IEE (London), vol. 93; pp. 429–457, 1946.
- J.G., Daugman, “Two-dimensional analysis of cortical receptive field profiles,” Vision Research, vol. 20; pp. 846–856, 1980.
- J.G., Daugman, “Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters,” J. of the Opt. Soc. of Amer., vol. A2(7); pp. 1160–1169, 1985.
- David J., Heeger, “A model for the extraction of image flow,” J. Opt. Soc. Amer., vol A4(8); pp. 1455–1471, 1987.
- David J. Heeger, “Models for motion perception,” Ph.D. thesis, CIS Department, Univ. of Pennsylvania, 1987. (Available as technical report MS-CIS-87-91.)
- S.G., Mallat, “Scale change versus scale space representation,” in Proc. First Int Conf. on Computer Vision, pp. 592–596, IEEE, London, 1987.
- P., Burt, “Fast algorithms for estimating local image properties,” Computer Vision, Graphics, and Image Processing, vol. 21; pp. 368–382, 1983.
- D.C., Burr and J., Ross, “Contrast sensitivity at high velocities,” Vision Research, vol. 22; pp. 479–484, 1982.
- P.E., Gill, W., Murray, and M.H., Wright, Practical Optimization. Academic Press: New York, 1981.
- R.A., Hummel and S.W., Zucker, “On the foundations of relaxing labelling processes,” IEEE Pami-5(3); pp. 267–287, 1983.
- D., Terzopoulos, “Regularization of inverse visual problems involving discontinuities,” IEEE Pami-8(4), pp. 413–424, 1986.
- T., Poggio, V., Torre, and C., Koch, “Computational vision and regularization theory,” Nature, vol. 317(6035); pp. 314–319, 1985.
- D., Marr and E., Hildreth, “Theory of edge detection,” Proc. Roy. Soc. (London), vol. B207; pp. 187–217, 1980.
- D.E., Rummelhart and J.L., McClelland, eds., Parallel Distributed Processing: Explorations in the Microstructure of Cognition. Mit Press: Cambridge, Mass., 1986.
- B.B., Mandlebrot, The Fractal Geometry of Nature. W.H. Freeman: New York, 1983.
- M.H., DeGroot, Probability and Statistics. Addison-Wesley: Menlo Park, Calif., 1975.
- J., Melsa and D., Cohn, Decision and Estimation Theory. McGraw-Hill: New York, 1978.
- E.H., Adelson and J.A., Movshon, “Phenomenal coherence of moving visual patterns,” Nature vol. 300(5892); pp. 523–525, 1982.
- E.H. Adelson, Media-Technology Laboratory, MIT, personal communication.
- M.P., doCarmo, Differential Geometry of Curves and Surfaces. Prentice-Hall: Englewood Cliffs, N.J., 1976.
- E.H. Adelson and E. Simonelli, “Orthogonal pyramid transfers for image coding,” in PROC. SPIE, VISUAL COMMUN. and IMAGE PROC. II, pp. 50–58, Cambridge, MA, 1987.
- Optical flow using spatiotemporal filters
International Journal of Computer Vision
Volume 1, Issue 4 , pp 279-302
- Cover Date
- Print ISSN
- Online ISSN
- Kluwer Academic Publishers
- Additional Links
- Industry Sectors
- David J. Heeger (1)
- Author Affiliations
- 1. Vision Sciences Group, Media Laboratory, Massachusetts Institute of Technology, 02139, Cambridge, Massachusetts