Demo Data from EDBT 2026 / reviewers in the wild / expert
T. A. Burkitt
dblp:182/6813
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1992
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1
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 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision › motion estimation
optical flow |
0.0 | 1 | 1992 | Performance of optical flow techniques · CVPR 1992 |
Computer vision › 3D vision › motion estimation › optical flow
optical flow evaluation |
0.0 | 1 | 1992 | Performance of optical flow techniques · CVPR 1992 |
Computer vision › 3D vision › motion estimation
ego-motion estimation |
0.0 | 1 | 1992 | Performance of optical flow techniques · CVPR 1992 |
Methods — techniques the papers use, named apart from their topics
local differential model · 0.0global smoothness constraint · 0.0confidence measure · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1992 | Performance of optical flow techniquesabstractThe performance of six optical flow techniques is compared, emphasizing measurement accuracy. The most accurate methods are found to be the local differential approaches, where nu is computed explicitly in terms of a locally constant or linear model. Techniques using global smoothness constraints appear to produce visually attractive flow fields, but in general seem to be accurate enough for qualitative use only and insufficient as precursors to the computations of egomotion and 3D structures. It is found that some form of confidence measure/threshold is crucial for all techniques in order to separate the inaccurate from the accurate. Drawbacks of the six techniques are discussed.> John L. Barron, David J. Fleet, Steven S. Beauchemin, T. A. Burkitt |
CVPR | 4 |