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Proceedings Paper

Hopfield neural network for qualitative recognition of object motion based on optical flow
Author(s): Gabriella Convertino; Maddalena Brattoli; Arcangelo Distante
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Paper Abstract

A method for the recognition of moving objects from a sequence of time-varying images is presented. The method consists of two phases: an estimation phase of optical flow field and an interpretation phase where a qualitative analysis of optical flow patterns is performed. The two phases interact each other in order to provide a final map in which areas of the image interested by the same motion are isolated and classified. For the estimation phase a gradient-based approach has been selected, that provides a linear optical flow map. In the interpretation phase the optical flow field is regarded as a 2D linear system of differential equations and then the geometric theory of differential equations is used. The whole algorithm is implemented by means of an Hopfield neural network. Experimental results on synthetic images are given.

Paper Details

Date Published: 10 June 1994
PDF: 10 pages
Proc. SPIE 2232, Signal Processing, Sensor Fusion, and Target Recognition III, (10 June 1994); doi: 10.1117/12.177740
Show Author Affiliations
Gabriella Convertino, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)
Maddalena Brattoli, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)
Arcangelo Distante, Istituto Elaborazione Segnali ed Immagini/CNR (Italy)

Published in SPIE Proceedings Vol. 2232:
Signal Processing, Sensor Fusion, and Target Recognition III
Ivan Kadar; Vibeke Libby, Editor(s)

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