EDBT 2026 Demo / reviewers in the wild / expert
Pär Fornland
dblp:88/2764
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 1997
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-authorArtificial intelligence and machine learning · 1 · 1 first-author
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Artificial intelligence
1 paper |
3D vision · 100% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision
stereo vision |
0.0 | 1 | 1997 | A robust and convergent iterative approach for determining the dominant plane from two views without correspondence and calibration · CVPR 1997 |
Computer vision › 3D vision › stereo vision
uncalibrated stereo |
0.0 | 1 | 1997 | A robust and convergent iterative approach for determining the dominant plane from two views without correspondence and calibration · CVPR 1997 |
Methods — techniques the papers use, named apart from their topics
planar projectivity estimation · 0.0iterative two-step optimization · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1997 | A robust and convergent iterative approach for determining the dominant plane from two views without correspondence and calibrationabstractA robust, iterative approach is introduced for finding the dominant plane in a scene using binocular vision. Neither camera calibration nor stereo correspondence is required. Recently L. Cohen (1996) formalized a framework guaranteeing (local) convergence of iterative two-step methods. In this paper, the framework is adopted, with a global step using tentative matches to estimate the planar projectivity, and a local step attempting to solve the stereo correspondence. A detected point in the first image is matched to an auxiliary point in the second image, on the line joining the transformed first image point, and its closest detected second image point. Convergence is assured, while achieving robustness to both mismatching and non-coplanar points. Pär Fornland, Christoph Schnörr |
CVPR | 1 |
| 1995 | Direct Obstacle Detection and Motion from Spatio-Temporal Derivatives
Pär Fornland |
CAIP | 1 |