Name
Abstract | ||
|---|---|---|
Given a set of point correspondences in an uncalibrated image pair, we can estimate the fundamental matrix, which can be used in calculating several geometric properties of the images. Among the several existing estimation methods, nonlinear methods can yield accurate results if an approximation to the true solution is given, whereas linear methods are inaccurate but no prior knowledge about the solution is required. Usually a linear method is employed to initialize a nonlinear method, but this sometimes results in failure when the linear approximation is far from the true solution. We herein describe an alternative, or complementary, method for the initialization. The proposed method minimizes the algebraic error, making sure that the results have the rank-2 property, which is neglected in the conventional linear method. Although an approximation is still required in order to obtain a feasible algorithm, the method still outperforms the conventional linear 8-point method, and is even comparable to Sampson error minimization. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11949534_123 | PSIVT |
Keywords | Field | DocType |
linear method,one-dimensional search,conventional linear method,true solution,algebraic error,sampson error minimization,linear approximation,existing estimation method,reliable epipole estimation,nonlinear method,8-point method,fundamental matrix | Nonlinear system,Algebraic number,Computer science,Image processing,Minimisation (psychology),Minification,Artificial intelligence,Fundamental matrix (computer vision),Linear approximation,Mathematical optimization,Pattern recognition,Algorithm,Initialization | Conference |
Volume | ISSN | ISBN |
4319 | 0302-9743 | 3-540-68297-X |
Citations | PageRank | References |
6 | 0.50 | 5 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tsuyoshi Migita | 1 | 17 | 2.78 |
| Takeshi Shakunaga | 2 | 192 | 43.46 |