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Stable Bounded Canonical Sets and Image Matching

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Book cover Energy Minimization Methods in Computer Vision and Pattern Recognition (EMMCVPR 2005)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 3757))

Abstract

A common approach to the image matching problem is representing images as sets of features in some feature space followed by establishing correspondences among the features. Previous work by Huttenlocher and Ullman [1] shows how a similarity transformation – rotation, translation, and scaling – between two images may be determined assuming that three corresponding image points are known. While robust, such methods suffer from computational inefficiencies for general feature sets. We describe a method whereby the feature sets may be summarized using the stable bounded canonical set (SBCS), thus allowing the efficient computation of point correspondences between large feature sets. We use a notion of stability to influence the set summarization such that stable image features are preferred.

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Author information

Authors and Affiliations

  1. Department of Computer Science, Drexel University,  

    John Novatnack, Trip Denton & Ali Shokoufandeh

  2. Computational Vision and Active Perception Laboratory, Department Of Numerical Analysis and Computer Science, KTH, Stockholm, Sweden

    Lars Bretzner

Authors
  1. John Novatnack
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  2. Trip Denton
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  3. Ali Shokoufandeh
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  4. Lars Bretzner
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Editor information

Editors and Affiliations

  1. Department of CISE, University of Florida, Gainesville, FL, USA

    Anand Rangarajan

  2. University of Florida, 32611, Gainesville, FL, USA

    Baba Vemuri

  3. Department of Statistics, University of California, Los Angeles

    Alan L. Yuille

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© 2005 Springer-Verlag Berlin Heidelberg

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Novatnack, J., Denton, T., Shokoufandeh, A., Bretzner, L. (2005). Stable Bounded Canonical Sets and Image Matching. In: Rangarajan, A., Vemuri, B., Yuille, A.L. (eds) Energy Minimization Methods in Computer Vision and Pattern Recognition. EMMCVPR 2005. Lecture Notes in Computer Science, vol 3757. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11585978_21

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  • DOI: https://doi.org/10.1007/11585978_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-30287-2

  • Online ISBN: 978-3-540-32098-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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