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Proceedings Paper

Probabilistic multichannel optical flow analysis based on a multipurpose visual representation of image sequences
Author(s): Oscar Nestares; Rafael Fonolla Navarro
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Paper Abstract

Directional filters are not normally used as pre-filters for optical flow estimation because orientation selectivity tends to increase the aperture problem. Despite this fact, here we apply a subband decomposition using directional spatio-temporal filters at different resolution to discriminate multiple motions at the same location. We first obtain multiple estimates of the velocity by applying the classic gradient constraint to the output of each filter. Spatio-temporal gradients of GD2 channel response are easily obtained as linear combinations of the set of 10 separable GD3 channel responses, which constitutes a multipurpose scheme for visual representation of image sequences. Then, we obtain an overdetermined linear system by imposing local constant velocity. This system is solved by least-squares yielding an estimate of the velocity and its covariance matrix. After segmenting the resulting 6 X 3 velocity estimates we combine them using Bayesian probability rules. Segmentation maintains the ability to represent multiple motions, while the combination of estimates reduces the aperture problem. Results for synthetic and real sequences are highly satisfactory. Mean errors in complex standard sequences are below those provided by most published methods.

Paper Details

Date Published: 19 May 1999
PDF: 12 pages
Proc. SPIE 3644, Human Vision and Electronic Imaging IV, (19 May 1999); doi: 10.1117/12.348479
Show Author Affiliations
Oscar Nestares, Instituto de Optica (Spain)
Rafael Fonolla Navarro, Instituto de Optica (Spain)

Published in SPIE Proceedings Vol. 3644:
Human Vision and Electronic Imaging IV
Bernice E. Rogowitz; Thrasyvoulos N. Pappas, Editor(s)

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