A Neural Network Based on Biological Vision Learning and Its Application on Robot

  • Ying Gao
  • Xiaodan Lu
  • Liming Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3498)


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.


Neural Network Receptive Field Input Image Training Phase Video Frame 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Ying Gao
    • 1
  • Xiaodan Lu
    • 1
  • Liming Zhang
    • 1
  1. 1.Dept. Electronic EngineeringFudan UniversityShanghaiChina

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