A neural network based on biological vision learning and its application on robot

Authors:
Ying Gao;Xiaodan Lu;Liming Zhang
Affiliations:
Dept. Electronic Engineering, Fudan University, Shanghai, China;Dept. Electronic Engineering, Fudan University, Shanghai, China;Dept. Electronic Engineering, Fudan University, Shanghai, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

Citing 3
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Abstract

This paper proposes a neural network called “Hierarchical Overlapping Sensory Mapping (HOSM)”, motivated by the structure of receptive fields in biological vision. To extract the features from these receptive fields, a method called Candid covariance-free Incremental Principal Component Analysis (CCIPCA) is used to automatically develop a set of orthogonal filters. An application of HOSM on a robot with eyes shows that the HOSM algorithm can pay attention to different targets and get its cognition for different environments in real time.