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10 June 1994 Hopfield neural network for qualitative recognition of object motion based on optical flow
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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.
© (1994) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Gabriella Convertino, Maddalena Brattoli, and Arcangelo Distante "Hopfield neural network for qualitative recognition of object motion based on optical flow", Proc. SPIE 2232, Signal Processing, Sensor Fusion, and Target Recognition III, (10 June 1994); https://doi.org/10.1117/12.177740
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