Name
Abstract | ||
|---|---|---|
We give a formal definition of geometric fitting in a way that suits computer vision applications. We point out that the performance of geometric fitting should be evaluated in the limit of small noise rather than in the limit of a large number of data as recommended in the statistical literature. Taking the KCR lower bound as an optimality requirement and focusing on the linearized constraint case, we compare the accuracy of Kanataniýs renormalization with maximum likelihood (ML) approaches including the FNS of Chojnacki et al. and the HEIV of Leedan and Meer. Our analysis reveals the existence of a method superior to all these. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1109/3DIM.2005.49 | 3DIM |
Keywords | Field | DocType |
polynomials,application software,lower bound,computer science,computer vision,maximum likelihood,maximum likelihood estimation,fitting | Renormalization,Applied mathematics,Computer vision,Mathematical optimization,Computer science,Upper and lower bounds,Surface fitting,Maximum likelihood,Formal description,Artificial intelligence | Conference |
ISBN | Citations | PageRank |
0-7695-2327-7 | 7 | 0.56 |
References | Authors | |
13 | 1 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kenichi Kanatani | 1 | 1468 | 320.07 |