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International Symposium on Neural Networks

ISNN 2006: Advances in Neural Networks - ISNN 2006 pp 404–410Cite as

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

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,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.

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References

  • Treisman, A., Gelade, G.: A Feature-Integration Theory of Attention. Cognit. Psychol. 12, 97–136 (1980)

    Article  Google Scholar 

  • Wolfe, J.M.: Guided Search 2. 0: A Revised Model of Visual Search. Psychonomic Bulletin & Review 1(2), 202–238 (1994)

    Article  Google Scholar 

  • Breazeal, C., Scassellati, B.: A Context-Dependent Attention System for a Social Robot. In: Proc. Int. Joint Conf. Artificial Intelligence, pp. 1146–1151 (1999)

    Google Scholar 

  • Triesch, J., von der Malsburg, C.: Democratic Integration: Self-organized Integration of Adaptive Cues. Neural Computation 13(9), 2049–2074 (2001)

    Article  MATH  Google Scholar 

  • Weng, J., Zhang, Y., Hwang, W.: Incremental Hierarchical Discriminant Regression for Online Image Classification. In: Proceedings of Sixth International Conference on Document Analysis and Recognition, September 10-13, pp. 476–480 (2001)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Electronic Engineer Department, Fudan University, Shanghai, 200433, China

    Chenlei Guo & Liming Zhang

Authors
  1. Chenlei Guo
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  2. Liming Zhang
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Editor information

Editors and Affiliations

  1. Department of Mechanical and Automation Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong, China

    Jun Wang

  2. Computational Intelligence Laboratory, School of Computer Science and Engineering, University of Electronic Science and Technology of China, 610054, Chengdu, P.R. China

    Zhang Yi

  3. Department of Electrical Engineering, University of Louisville, 40292, Louisville, KY, U.S.A

    Jacek M. Zurada

  4. Laboratory for Computational Biology, Shanghai Center for Systems Biomedicine, 800 Dong Chuan Rd., 200240, Shanghai, China

    Bao-Liang Lu

  5. School of Electrical and Electronic Engineering, University of Manchester, UK

    Hujun Yin

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© 2006 Springer-Verlag Berlin Heidelberg

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Cite this paper

Guo, C., Zhang, L. (2006). An Attention Selection System Based on Neural Network and Its Application in Tracking Objects. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_59

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  • DOI: https://doi.org/10.1007/11760023_59

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34437-7

  • Online ISBN: 978-3-540-34438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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