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Study on Object Recognition Based on Independent Component Analysis

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Advances in Neural Networks – ISNN 2004 (ISNN 2004)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 3173))

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Abstract

This paper proposes a new scheme based on Independent Component Analysis (ICA) for object recognition with affine transformation. For different skewed shapes of recognized object, an invariant descriptor can be extracted by ICA, and it can solve some skewed object recognition problems. Simulation results show that the proposed method can recognize not only skewed objects but also misshapen objects, and it has a better performance than other traditional methods, such as Fourier method in object recognition.

This research was supported by a grant from the National Science Foundation (NSF 60171036, 30370392), China.

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Author information

Authors and Affiliations

  1. Dept. E.E, Fudan University, Shanghai, 200433, China

    Xuming Huang, Chengming Liu & Liming Zhang

Authors
  1. Xuming Huang
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  2. Chengming Liu
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  3. Liming Zhang
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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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Huang, X., Liu, C., Zhang, L. (2004). Study on Object Recognition Based on Independent Component Analysis. In: Yin, FL., Wang, J., Guo, C. (eds) Advances in Neural Networks – ISNN 2004. ISNN 2004. Lecture Notes in Computer Science, vol 3173. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28647-9_118

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  • DOI: https://doi.org/10.1007/978-3-540-28647-9_118

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-22841-7

  • Online ISBN: 978-3-540-28647-9

  • eBook Packages: Springer Book Archive

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