Demo Data from EDBT 2026 / reviewers in the wild / expert
Kenneth M. Dawson
dblp:47/874
· DBLP profile ↗
2ranked-venue papers
2as first author
0since 2021 · last 1992
0000-0002-0568-6588ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 2 · 2 first-authorSystems, architecture and hardware · 1 · 1 first-authorGraphics, computer vision, multimedia, augmented reality and games · 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 · 56% Image recognition and object detection · 44% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Image recognition and object detection
object recognition |
0.0 | 1 | 1992 | 3-D Object Recognition using Passively Sensed Range Data · ECCV 1992 |
Computer vision › 3D vision › 3d object recognition
range image object recognition |
0.0 | 1 | 1992 | 3-D Object Recognition using Passively Sensed Range Data · ECCV 1992 |
Computer vision › 3D vision
range sensing |
0.0 | 1 | 1992 | 3-D Object Recognition using Passively Sensed Range Data · ECCV 1992 |
Methods — techniques the papers use, named apart from their topics
range data recognition · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1992 | 3-D Object Recognition using Passively Sensed Range Data
Kenneth M. Dawson, David Vernon |
ECCV | 1 |
| 1991 | On the usefulness of optical flow for robotic part manipulationabstractThis paper describes experience gained in using optical flow fields, arising from constrained camera motion, to estimate range information, for robotic part manipulation. Several key issues in robot vision are addressed. These include the computation of optical flow, computation of depth, interpolation of depth values, model construction, object recognition, and pose estimation. The paper provides an example of the approach and evaluates the system's performance in the context of its applicability to part manipulation.> Kenneth M. Dawson, Massimo Tistarelli, David Vernon |
IROS | 1 |