Title :
Unsupervised learning method to extract object locations from local visual signals
Author :
Shibata, K. ; Okabe, Y.
Author_Institution :
Res. Center for Adv. Sci. & Technol., Tokyo Univ., Japan
fDate :
27 Jun-2 Jul 1994
Abstract :
In order to acknowledge the object location or size, one has to integrate visual signals from many local retinal neurons. In this paper the authors propose an unsupervised learning method to realize this ability using a temporal smoothness assumption. The authors have confirmed by simulation that using the learning method, one can extract an object location or size in a simple environment
Keywords :
computer vision; neural nets; neurophysiology; object recognition; unsupervised learning; visual perception; local retinal neurons; local visual signals; object locations extraction; temporal smoothness; unsupervised learning; Data mining; Intelligent sensors; Jacobian matrices; Learning systems; Neural networks; Neurons; Retina; Shape; Smoothing methods; Unsupervised learning;
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.1994.374387