Name
Abstract | ||
|---|---|---|
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 |
Year | DOI | Venue |
|---|---|---|
1993 | 10.1109/CVPR.1993.341020 | New York, NY |
Keywords | Field | DocType |
Bayes methods,Kalman filters,differential geometry,file organisation,image matching,invariance,splines (mathematics),3D curves,Bayesian theory,Kalman filter,curve matching,data curves,fast rigid matching,geometric hashing,hash tables,local minima,subvoxel precision,uncertainty | Invariant (physics),Pattern recognition,Computer science,Kalman filter,Maxima and minima,Artificial intelligence,Differential geometry,Invariant (mathematics),Geometric hashing,Bayesian probability,Hash table | Conference |
Volume | Issue | ISSN |
1993 | 1 | 1063-6919 |
ISBN | Citations | PageRank |
0-8186-3880-X | 3 | 2.41 |
References | Authors | |
0 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| André Guéziec | 1 | 733 | 96.14 |
| Nicholas Ayache | 2 | 10804 | 1654.36 |