Stable Bounded Canonical Sets and Image Matching John Novatnack 1 , Trip Denton 1 , Ali Shokoufandeh 1 , and Lars Bretzner 2 1 Department of Computer Science, Drexel University {jmn27,tdenton,ashokouf}@cs.drexel.edu 2 Computational Vision and Active Perception Laboratory, Department Of Numerical Analysis and Computer Science, KTH, Stockholm, Sweden, bretzner@nada.kth.se Abstract. A common approach to the image matching problem is representing images as sets of features in some feature space followed by establishing corre- spondences among the features. Previous work by Huttenlocher and Ullman [1] shows how a similarity transformation - rotation, translation, and scaling - be- tween two images may be determined assuming that three corresponding image points are known. While robust, such methods suffer from computational ineffi- ciencies for general feature sets. We describe a method whereby the feature sets may be summarized using the Stable Bounded Canonical Set (SBCS), thus allow- ing 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. Fig. 1. A) Blob and ridge feature extraction with centroids of blobs and ridges denoted, B) Stable Bounded Canonical Set (SBCS) construction, C) Determine transformation, D) Outline shows transformation determined from SBCS. 1 Introduction Image matching remains an important open problem in computer vision with numerous applications. One common approach to the problem is to represent images as features