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An Attention Selection System Based on Neural Network and Its Application in Tracking Objects

  • Chenlei Guo
  • Liming Zhang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3972)

Abstract

In this paper an attention selection system based on neural network is proposed, which combines supervised and unsupervised learning reasonably. A value system and memory tree with update ability are regarded as teachers to adjust the weights of neural network. Both bottom-up and top-down part are to simulate two-stage hypothesis of attention selection in biological vision. The system is able to track objects that it is interested in. Whenever it lost focus on tracked object, it can find the object again in a short time.

Keywords

Leaf Node Connection Weight Unsupervised Learning Gabor Filter Social Robot 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Chenlei Guo
    • 1
  • Liming Zhang
    • 1
  1. 1.Electronic Engineer DepartmentFudan UniversityShanghaiChina

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