Representing stereo data with the Delaunay triangulation
Artificial Intelligence
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Iterative pose estimation using coplanar feature points
Computer Vision and Image Understanding
Rigidity Checking of 3D Point Correspondences Under Perspective Projection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Structural Matching by Discrete Relaxation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Matching With a Dual-Step EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
The String-to-String Correction Problem
Journal of the ACM (JACM)
Distortion Invariant Object Recognition in the Dynamic Link Architecture
IEEE Transactions on Computers
Computation of Normalized Edit Distance and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Efficient Relational Matching with Local Edit Distance
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
A Binary Linear Programming Formulation of the Graph Edit Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
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This paper presents work aimed at rendering the dual-step EM algorithm of Cross and Hancock more efficient. The original algorithm integrates the processes of point-set alignment and correspondence. The consistency of the pattern of correspondence matches on the Delaunay triangulation of the points is used to gate contributions to the expected log-likelihood function for point-set alignment parameters. However, in its original form the algorithm uses a dictionary of structure-preserving mappings to asses the consistency of match. This proves to be a serious computational bottleneck. In this paper, we show how graph edit-distance can be used to compute the correspondence probabilities more efficiently. In a sensitivity analysis, we show that the edit distance method is not only more efficient, it is also more accurate than the dictionary-based method.