Stable Bounded Canonical Sets and Image Matching

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. 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.