OPTIC FLOW ESTIMATION BY A HOPFIELD NEURAL-NETWORK USING GEOMETRICAL CONSTRAINTS

Sparse optic flow maps are general enough to obtain useful information about camera motion. Usually, correspondences among features over an image sequence are estimated by radiometric similarity. When the camer a moves under known conditions, global geometrical constraints can be introduced in order to obtain a more robust estimation of the optic fl ow. In this paper, a method is proposed for the computation of a robus t sparse optic flow (OF) which integrates the geometrical constraints induced by camera motion to verify the correspondences obtained by rad iometric-similarity-based techniques. A raw OF map is estimated by mat ching features by correlation. The verification of the resulting corre spondences is formulated as an optimization problem that is implemente d on a Hopfield neural network (HNN). Additional constraints imposed i n the energy function permit us to achieve a subpixel accuracy in the image locations of matched features. Convergence of the HNN is reached in a small enough number of iterations to make the proposed method su itable for real-time processing. It is shown that the proposed method is also suitable for identifying independently moving objects in front of a moving vehicle.