Name
Abstract | ||
|---|---|---|
D reconstruction with missing data has been a challenging computer vision task since the late 90s. This paper proposes a novel metric reconstruction al- gorithm dealing with the missing data problem. The algorithm is the adaption of the Fast Alternation method published by us in CAIP2007. We concentrate on metric instead of affine reconstruction because the quality of metric recon- structionissignificantlybetterasitisdemonstratedinthisstudy. Thesolution is an alternation which consists of several substeps. All of these substeps are optimal with respect to the parameters that are being optimized. It is proved that the proposed algorithm converges to a local minimum. The solutions to the optimization subproblems in our approach are given by closed-form formulas, therefore the proposed method is relatively fast. |
Year | Venue | Keywords |
|---|---|---|
2008 | BMVC | computer vision,missing data |
Field | DocType | Citations |
Data mining,Pattern recognition,Affine reconstruction,Computer science,Algorithm,Artificial intelligence,Missing data problem,Missing data,Alternation (linguistics) | Conference | 1 |
PageRank | References | Authors |
0.36 | 7 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ákos Pernek | 1 | 7 | 1.84 |
| Levente Hajder | 2 | 43 | 12.55 |
| Csaba Kazó | 3 | 6 | 1.16 |