Abstract
Optical snow is a natural type of image motion that results when the observer moves laterally relative to a cluttered 3D scene. An example is an observer moving past a bush or through a forest, or a stationary observer viewing falling snow. Optical snow motion is unlike standard motion models in computer vision, such as optical flow or layered motion since such models are based on spatial continuity assumptions. For optical snow, spatial continuity cannot be assumed because the motion is characterized by dense depth discontinuities. In previous work, we considered the special case of parallel optical snow. Here we generalize that model to allow for non-parallel optical snow. The new model describes a situation in which a laterally moving observer tracks an isolated moving object in an otherwise static 3D cluttered scene. We argue that despite the complexity of the motion, sufficient constraints remain that allow such an observer to navigate through the scene while tracking a moving object.
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References
M. S. Langer and R. Mann. Dimensional analysis of image motion. In IEEE International Conference on Computer Vision, pages 155–162, 2001.
R. Mann and M. S. Langer. Optical snow and the aperture problem. In International Conference on Pattern Recognition, Quebec City, Canada, Aug. 2002.
A.B. Watson and A.J. Ahumada. Model of human visual-motion sensing. Journal of the Optical Society of America A, 2(2):322–342, 1985.
H.C. Longuet-Higgins and K. Prazdny. The interpretation of a moving retinal image. Proceedings of the Royal Society of London B), B-208:385–397, 1980.
D.J. Heeger. Optical flow from spatiotemporal filters. In First International Conference on Computer Vision, pages 181–190, 1987.
D. J. Fleet. Measurement of Image Velocity. Kluwer Academic Press, Norwell, MA, 1992.
N.M. Grzywacz and A.L. Yuille. A model for the estimate of local image velocity by cells in the visual cortex. Proceedings of the Royal Society of London. B, 239:129–161, 1990.
E P Simoncelli and D J Heeger. A model of neural responses in visual area mt. Vision Research, 38(5):743–761, 1998.
M. Shizawa and K. Mase. A unified computational theory for motion transparency and motion boundaries based on eigenenergy analysis. In IEEE Conference on Computer Vision and Pattern Recognition, pages 289–295, 1991.
P. Milanfar. Projection-based, frequency-domain estimation of superimposed translational motions. Journal of the Optical Society of America A, 13(11):2151–2162, November 1996.
D. J. Fleet and K. Langley. Computational analysis of non-fourier motion. Vision Research, 34(22):3057–3079, 1994.
S.S. Beauchemin and J.L. Barron. The frequency structure of 1d occluding image signals. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(2):200–206, February 2000.
E. Trucco and A. Verri. Introductory Techniques for 3-D Computer Vision. Prentice-Hall, 1998.
M. Lappe and J. P. Rauschecker. A neural network for the processing of optical flow from egomotion in man and higher mammals. Neural Computation, 5:374–391, 1993.
S. W. Zucker and L. Iverson. From orientation selection to optical flow. Computer Vision Graphics and Image Processing, 37:196–220, 1987.
E.H. Adelson and J.R. Bergen. Spatiotemporal energy models for the perception of motion. Journal of the Optical Society of America A, 2(2):284–299, 1985.
R. S. Zemel and P. Dayan. Distributional population codes and multiple motion models. In D. A. Cohn M. S. Kearns, S. A. Solla, editor, Advances in Neural Information Processing Systems 11, pages 768–784, Cambridge, MA, 1999. MIT Press.
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© 2002 Springer-Verlag Berlin Heidelberg
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Langer, M.S., Mann, R. (2002). Tracking through Optical Snow. In: Bülthoff, H.H., Wallraven, C., Lee, SW., Poggio, T.A. (eds) Biologically Motivated Computer Vision. BMCV 2002. Lecture Notes in Computer Science, vol 2525. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36181-2_18
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DOI: https://doi.org/10.1007/3-540-36181-2_18
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