Name
Abstract | ||
|---|---|---|
We compare the convergence performance of different numerical schemes for computing the fundamental matrix from point correspondences over two images. First, we state the problem and the associated KCR lower bound. Then, we describe the algorithms of three well-known methods: FNS, HEIV, and renormalization. We also introduce Gauss-Newton iterations as a new method for fundamental matrix computation. For initial values, we test random choice, least squares, and Taubin's method. Experiments using simulated and real images reveal different characteristics of each method. Overall, FNS exhibits the best convergence properties. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1093/ietisy/e90-d.2.579 | IEICE - Transactions on Information and Systems |
Keywords | DocType | Volume |
convergence property,performance evaluation,high accuracy fundamental matrix,fundamental matrix computation,well-known method,convergence performance,associated kcr,different characteristic,fundamental matrix,new method,different numerical scheme,gauss-newton iteration | Journal | E90-D |
Issue | ISSN | Citations |
2 | 0916-8532 | 10 |
PageRank | References | Authors |
0.65 | 10 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kenichi Kanatani | 1 | 1468 | 320.07 |
| Yasuyuki Sugaya | 2 | 267 | 25.45 |