Name
Abstract | ||
|---|---|---|
A very compact algorithm is presented for fundamental matrix computation from point correspondences over two images. The computation
is based on the strict maximum likelihood (ML) principle, minimizing the reprojection error. The rank constraint is incorporated
by the EFNS procedure. Although our algorithm produces the same solution as all existing ML-based methods, it is probably
the most practical of all, being small and simple. By numerical experiments, we confirm that our algorithm behaves as expected.
|
Year | DOI | Venue |
|---|---|---|
2010 | 10.2197/ipsjtcva.2.59 | Pacific-Rim Symposium on Image and Video Technology |
Keywords | Field | DocType |
point correspondence,fundamental matrix computation,numerical experiment,existing ml-based method,compact fundamental matrix computation,reprojection error,efns procedure,compact algorithm,strict maximum likelihood,rank constraint,fundamental matrix,maximum likelihood | Reprojection error,Mathematical optimization,Point correspondence,Pattern recognition,Computer science,Maximum likelihood,Algorithm,Projection (linear algebra),Artificial intelligence,Fundamental matrix (computer vision),Computation | Journal |
Volume | ISSN | Citations |
2 | 0302-9743 | 7 |
PageRank | References | Authors |
0.51 | 20 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kenichi Kanatani | 1 | 1468 | 320.07 |
| Yasuyuki Sugaya | 2 | 267 | 25.45 |