Optic flow estimation by a Hopfield neural network using geometrical constraints |
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Authors: | G Convertino E Stella A Branca A Distante |
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Affiliation: | (1) Istituto Elaborazione Segnali ed Immagini - C.N.R., Via Amendola 166/5, I-70126 Bari, Italy , IT |
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Abstract: | 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 camera moves under known conditions, global
geometrical constraints can be introduced in order to obtain a more robust estimation of the optic flow. In this paper, a
method is proposed for the computation of a robust sparse optic flow (OF) which integrates the geometrical constraints induced
by camera motion to verify the correspondences obtained by radiometric-similarity-based techniques. A raw OF map is estimated
by matching features by correlation. The verification of the resulting correspondences is formulated as an optimization problem
that is implemented on a Hopfield neural network (HNN). Additional constraints imposed in 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 suitable 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.
Received: 26 December 1995 / Accepted: 20 February 1997 |
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Keywords: | : Optical flow – Hopfield neural network – Features Matching – Trajectory geometric constraints |
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