Intrinsic Constraints in Space-Time Filtering: A New Approach to Representing Uncertainty in Low-Level Vision
Issue No. 03 - March (1992 vol. 14)
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/34.120330
<p>Describes how, in the process of extracting the optical flow through space-time filtering, one has to consider the constraints associated with the motion uncertainty, as well as the spatial and temporal sampling rates of the sequence of images. The motion uncertainty satisfies the Cramer-Rao (CR) inequality, which is shown to be a function of the filter parameters. On the other hand, the spatial and temporal sampling rates have lower bounds, which depend on the motion uncertainty, the maximum support in the frequency domain, and the optical flow. These lower bounds on the sampling rates and on the motion uncertainty are constraints that constitute an intrinsic part of the computational structure of space-time filtering. The author shows that if he uses these constraints simultaneously, the filter parameters cannot be arbitrarily determined but instead have to satisfy consistency constraints. By using explicit representations of uncertainties in extracting visual attributes, one can constrain the range of values assumed by the filter parameters.</p>
spatial sampling rates; Cramer-Rao inequality; parameter estimation; intrinsic constraints; picture processing; space-time filtering; low-level vision; optical flow; motion uncertainty; temporal sampling rates; parameter estimation; pattern recognition; picture processing; spatial filters
R. Jasinschi, "Intrinsic Constraints in Space-Time Filtering: A New Approach to Representing Uncertainty in Low-Level Vision," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 14, no. , pp. 353-366, 1992.