New developments on geometric hashing for curve matching

The problem of fast rigid matching of 3D curves with subvoxel precision is addressed. More invariant parameters are used, and new hash tables are implemented in order to process larger and more complex sets of data curves. There exists a Bayesian theory of geometric hashing that explains why local minima are not really a problem. The more likely transformation always wins. It is also possible to predict the uncertainty on the match with the help of the Kalman filter, and compare it with real measures.<
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