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A New Computational Model of Biological Vision for Stereopsis

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Advances in Neural Networks - ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3174))

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Abstract

This paper designs a new adaptive disparity filter which implements the binocular matching of disparity features in stereo images, and constructs a computational model as well as a stereopsis system based on the disparity filter along with mechanism of biological vision by integrating it with Grossberg’s FACADE theory, to process real-world images of 3-D scenes. By using this stereopsis system, depth perception of surfaces in real-world stereo images is simulated and realized.

Supported by the Distinguished Young Scholars Fund of China (60225015), Natural Science Foundation of China (60171003), Ministry of Science and Technology of China (2001CCA04100) and Ministry of Education of China (TRAPOYT Project).

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References

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Author information

Authors and Affiliations

  1. Department of Automatic Control, National University of Defense Technology, Changsha, Hunan, 410073, P.R.C.

    Baoquan Song, Zongtan Zhou, Dewen Hu & Zhengzhi Wang

Authors
  1. Baoquan Song
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  2. Zongtan Zhou
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  3. Dewen Hu
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  4. Zhengzhi Wang
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Editor information

Editors and Affiliations

  1. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, China

    Fu-Liang Yin

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

    Jun Wang

  3. School of Electronic and Information Engineering, Dalian University of Technology, 116023, Dalian, Liaoning, China

    Chengan Guo

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© 2004 Springer-Verlag Berlin Heidelberg

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Song, B., Zhou, Z., Hu, D., Wang, Z. (2004). A New Computational Model of Biological Vision for Stereopsis. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks - ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3174. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28648-6_84

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  • DOI: https://doi.org/10.1007/978-3-540-28648-6_84

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22843-1

  • Online ISBN: 978-3-540-28648-6

  • eBook Packages: Springer Book Archive

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