Name
Abstract | ||
|---|---|---|
We construct probabilistic generative models for the non-rigid matching of point-sets. Our formulation is explicitly Platonist. Beginning with a Platonist super point-set, we derive real-world point-sets through the application of four operations: i) spline-based warping, ii) addition of noise, iii) point removal and iii) amnesia regarding the pointto-point correspondences between the real-world point-sets and the Platonist source. Given this generative model, we are able to derive new non-quadratic distance measures w.r.t. the "forgotten" correspondences by a) eliminating the spline parameters from the generative model and by b) integrating out the Platonist super point-set. The result is a new non-quadratic distance measure which has the interpretation of weighted graph matching. The graphs are related in a straightfoward manner to the spline kernel used for non-rigid warping. Experimentally, we show that the new distance measure outperforms the conventional quadratic assignment distance measure when both distances use the same weighted graphs derived from the spline kernel. |
Year | Venue | Keywords |
|---|---|---|
1999 | EMMCVPR | new distance measure,probabilistic generative model,platonist source,spline kernel,spline parameter,platonist super point-set,non-rigid image matching,conventional quadratic assignment distance,real-world point-sets,generative model,new non-quadratic distance measure |
Field | DocType | ISBN |
Kernel (linear algebra),Spline (mathematics),Computer vision,Image warping,Computer science,Quadratic equation,Matching (graph theory),Artificial intelligence,Probabilistic logic,Generative model,Distance measures | Conference | 3-540-66294-4 |
Citations | PageRank | References |
5 | 0.52 | 25 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| A Rangarajan | 1 | 3698 | 367.52 |
| Haili Chui | 2 | 1034 | 58.44 |
| Eric Mjolsness | 3 | 1058 | 140.00 |