Title :
A simple feedforward neural network architecture for 3-D motion and structure estimation
Author :
Sun, Yi ; Bayoumi, Mohamed M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Queen´´s Univ., Kingston, Ont., Canada
Abstract :
A simple feedforward neural network system which is especially designed to tackle the problem of 3-D motion and structure parameter estimation from 2-D optical flow parameters has been proposed. Each node and the weight of every connection adopted in the network has its explicit physical meaning. The network embraces a self tuning scheme with an unsupervised learning rule to control the dynamics of the system. It also adopts a mechanism for preattentative focus that effectively suppresses the spurious solution of the estimation
Keywords :
feedforward neural nets; image sequences; motion estimation; neural net architecture; parameter estimation; unsupervised learning; 2D optical flow parameters; 3D motion estimation; 3D structure parameter estimation; feedforward neural network architecture; preattentative focus; self tuning scheme; system dynamics control; unsupervised learning rule; Control systems; Feedforward neural networks; Image motion analysis; Neural networks; Optical computing; Optical design; Optical fiber networks; Optical tuning; Parameter estimation; Unsupervised learning;
Conference_Titel :
Image Processing, 1996. Proceedings., International Conference on
Conference_Location :
Lausanne
Print_ISBN :
0-7803-3259-8
DOI :
10.1109/ICIP.1996.560848
