Rotation invariant spherical harmonic representation of 3D shape descriptors
Proceedings of the 2003 Eurographics/ACM SIGGRAPH symposium on Geometry processing
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
A Linear Programming Approach to Max-Sum Problem: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
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In this paper we compare images based on the constellation of their interest points. The fundamental technique for this comparison is our matching algorithm, that is capable to model miss- and multimatches, while enforcing one-to-one matches. We associate an energy function for the possible matchings. In order to find the matching with the lowest energy, we reformulate this energy function as Markov Random Field and determine the matching with the lowest energy by an efficient minimization strategy. In the experiments, we compare our algorithm against the normalized cross correlation and a naive forth-and-back best neighbor match algorithm.