Demo Data from EDBT 2026 / reviewers in the wild / expert
Avinoam Beinglass
dblp:182/4685
· 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 |
3D vision · 50% Image recognition and object detection · 50% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision › 3d object recognition
articulated object recognition |
0.0 | 1 | 1991 | Articulated object recognition, or: how to generalize the generalized Hough transform · CVPR 1991 |
Computer vision › Image recognition and object detection › object recognition
model-based object recognition |
0.0 | 1 | 1991 | Articulated object recognition, or: how to generalize the generalized Hough transform · CVPR 1991 |
Methods — techniques the papers use, named apart from their topics
generalized hough transform · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1991 | Articulated object recognition, or: how to generalize the generalized Hough transformabstractA method for model-based recognition of articulated objects in cluttered scenes is presented. The objects consist of rigid parts connected by rotary or prismatic joints. The method is based on an extension of the generalized Hough transform approach. It is applicable to various viewing transformations. Unlike previous methods there is no significant degradation in performance for recognition of articulated objects compared with the recognition of rigid objects containing similar amounts of information. The method was implemented and successfully tested for recognition of partially overlapping 2-D objects with rotary joints which have undergone rotation, translation, and scaling.> Avinoam Beinglass, Haim J. Wolfson |
CVPR | 1 |