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Invariant Feature Extraction and Object Shape Matching Using Gabor Filtering

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Recent Advances in Visual Information Systems (VISUAL 2002)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 2314))

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Abstract

Gabor filter-based feature extraction and its use in object shape matching are addressed. For the feature extraction multi-scale Gabor filters are used. From the analysis of the properties of the Gabor-filtered image, we know isolated dominant points generally exist on the object contour, when the filter design parameters are properly selected. The dominant points thus extracted are robust to the image noise, scaling, rotation, translation, and the minor projection deformation. Object shape matching in terms of a two-stage point matching is presented. First, a feature vector representation of the dominant point is used for initial matching. Secondly, the compatibility constraints on the distances and angles between point pairs are used for the final matching. Computer simulations with synthetic and real object images are included to show the feasibility of the proposed method.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Chiao Tung University, Hinchu, Taiwan

    Sun Shu-Kuo & Zen Chen

  2. Department of Information Management, Ming Chuan University, Taoyuan, Taiwan

    Chia Tsorng-Lin

Authors
  1. Sun Shu-Kuo
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  2. Zen Chen
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  3. Chia Tsorng-Lin
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Editor information

Editors and Affiliations

  1. Knowledge Systems Institute, 3420 Main Street, 60076, Skokie, IL, USA

    Shi-Kuo Chang

  2. Dept. of Comp. Science & Information Engineering, National Chiao Tung University, 1001 Ta HsuehRoad, Hsin Chu, Taiwan

    Zen Chen  & Suh-Yin Lee  & 

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© 2002 Springer-Verlag Berlin Heidelberg

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Shu-Kuo, S., Chen, Z., Tsorng-Lin, C. (2002). Invariant Feature Extraction and Object Shape Matching Using Gabor Filtering. In: Chang, SK., Chen, Z., Lee, SY. (eds) Recent Advances in Visual Information Systems. VISUAL 2002. Lecture Notes in Computer Science, vol 2314. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45925-1_9

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  • DOI: https://doi.org/10.1007/3-540-45925-1_9

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-43358-3

  • Online ISBN: 978-3-540-45925-5

  • eBook Packages: Springer Book Archive

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