Title :
Hopfield neural network for motion understanding
Author :
Convertino, G. ; Brattoli, M. ; Branca, A. ; Distante, A.
Author_Institution :
CNR, Bari, Italy
fDate :
27 Jun- 2 Jul 1994
Abstract :
A method for 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 (OF) field and an interpretation phase where a qualitative analysis of OF 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 OF field is regarded as a 2D linear system of differential equations and hence the geometric theory of differential equations is used. The whole algorithm is implemented by means of an Hopfield neural network (HNN)
Keywords :
Hopfield neural nets; differential equations; image sequences; motion estimation; object recognition; 2D linear system; Hopfield neural network; classification; differential equations; geometric theory; gradient-based approach; interpretation phase; isolation; linear optical flow map; motion understanding; moving object recognition; optical flow field estimation; qualitative analysis; time-varying image sequences; Differential equations; Geometrical optics; Hopfield neural networks; Image motion analysis; Image recognition; Linear systems; Optical computing; Pattern analysis; Performance analysis; Phase estimation;
Conference_Titel :
Neural Networks, 1994. IEEE World Congress on Computational Intelligence., 1994 IEEE International Conference on
Conference_Location :
Orlando, FL
Print_ISBN :
0-7803-1901-X
DOI :
10.1109/ICNN.1994.374824
