Stable Bounded Canonical Sets and Image Matching

  • John Novatnack
  • Trip Denton
  • Ali Shokoufandeh
  • Lars Bretzner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3757)


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.


Reference Image Scale Invariant Feature Transform Image Match Integer Programming Problem Improve Approximation Algorithm 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • John Novatnack
    • 1
  • Trip Denton
    • 1
  • Ali Shokoufandeh
    • 1
  • Lars Bretzner
    • 2
  1. 1.Department of Computer ScienceDrexel University 
  2. 2.Computational Vision and Active Perception Laboratory, Department Of Numerical Analysis and Computer ScienceKTH, StockholmSweden

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