Optical flow using spatiotemporal filters

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.

[1]  J. Daugman.  Uncertainty relation for resolution in space, spatial frequency, and orientation optimized by two-dimensional visual cortical filters. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[2]  D J Heeger,et al.  Model for the extraction of image flow. , 1987, Journal of the Optical Society of America. A, Optics and image science.

[3]  Peter J. Burt,et al.  Fast algorithms for estimating local image properties , 1982, Comput. Graph. Image Process..

[4]  E H Adelson,et al.  Spatiotemporal energy models for the perception of motion. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[5]  William B. Thompson,et al.  Disparity Analysis of Images , 1980, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Tomaso Poggio,et al.  Computational vision and regularization theory , 1985, Nature.

[7]  E. Adelson,et al.  Phenomenal coherence of moving visual patterns , 1982, Nature.

[8]  J. L. Melsa,et al.  Decision and Estimation Theory , 1981, IEEE Transactions on Systems, Man, and Cybernetics.

[9]  Andrew B. Watson,et al.  A look at motion in the frequency domain , 1983 .

[10]  Dennis Gabor,et al.  Theory of communication , 1946 .

[11]  M. Degroot,et al.  Probability and Statistics , 2021, Examining an Operational Approach to Teaching Probability.

[12]  T. Poggio,et al.  Visual hyperacuity: spatiotemporal interpolation in human vision , 1981, Proceedings of the Royal Society of London. Series B. Biological Sciences.

[13]  J. Daugman.  Two-dimensional spectral analysis of cortical receptive field profiles , 1980, Vision Research.

[14]  Y. Y. Zeevi,et al.  A model for separation of spatial and temporal information in the visual system , 1977, Biological Cybernetics.

[15]  Ellen C. Hildreth,et al.  Measurement of Visual Motion , 1984 .

[16]  Manfredo P. do Carmo,et al.  Differential geometry of curves and surfaces , 1976 .

[17]  A J Ahumada,et al.  Model of human visual-motion sensing. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[18]  J. van Santen,et al.  Elaborated Reichardt detectors. , 1985, Journal of the Optical Society of America. A, Optics and image science.

[19]  Joseph K. Kearney,et al.  Optical Flow Estimation: An Error Analysis of Gradient-Based Methods with Local Optimization , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[20]  James L. McClelland,et al.  Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations , 1986 .

[21]  Seong-Dae Kim,et al.  An error analysis of gradient-based methods , 1994, Signal Process..

[22]  Steven W. Zucker,et al.  On the Foundations of Relaxation Labeling Processes , 1983, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  D. Burr,et al.  Contrast sensitivity at high velocities , 1982, Vision Research.

[24]  Y. Y. Zeevi,et al.  A model for processing of movement in the visual system , 1979, Biological Cybernetics.

[25]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[26]  Ruzena Bajcsy,et al.  Models for motion perception , 1987 .

[27]  Philip E. Gill,et al.  Practical optimization , 1981 .

[28]  Benoit B. Mandelbrot,et al.  Fractal Geometry of Nature , 1984 .

[29]  Edward H. Adelson,et al.  Orthogonal Pyramid Transforms For Image Coding. , 1987, Other Conferences.

[30]  Demetri Terzopoulos,et al.  Regularization of Inverse Visual Problems Involving Discontinuities , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  D Marr,et al.  Theory of edge detection , 1979, Proceedings of the Royal Society of London. Series B. Biological Sciences.