Demo Data from EDBT 2026 / reviewers in the wild / expert
Chennubhotla S. Chakravarthy
dblp:182/4719
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1991
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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 |
Image recognition and object detection · 50% 3D vision · 50% | |
| Computer graphics and multimedia
1 paper |
Image and video processing · 100% |
Topics — the 4 heaviest of 4, 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 | 1991 | Pose clustering on constraints for object recognition · CVPR 1991 |
Computer vision › 3D vision › pose estimation
pose clustering |
0.0 | 1 | 1991 | Pose clustering on constraints for object recognition · CVPR 1991 |
Image and video processing › image matching
feature matching |
0.0 | 1 | 1991 | Pose clustering on constraints for object recognition · CVPR 1991 |
Image and video processing › feature detection
hough transform |
0.0 | 1 | 1991 | Pose clustering on constraints for object recognition · CVPR 1991 |
Methods — techniques the papers use, named apart from their topics
spatial relations · 0.0one-dimensional hough transforms · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1991 | Pose clustering on constraints for object recognitionabstractThe authors investigate a way of improving Hough transform performance using the spatial relations that exist between model features. Clusters of scene features possibly belonging to a model are grouped by a series of one-dimensional Hough transforms. The results presented illustrate the effectiveness of the methods.> Chennubhotla S. Chakravarthy, Rangachar Kasturi |
CVPR | 1 |