Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page

Avinoam Beinglass

dblp:182/4685 · DBLP profile ↗
← Back
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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › 3d object recognition
articulated object recognition
0.011991
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
0.011991
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
YearPublicationVenuePosition
1991 Articulated object recognition, or: how to generalize the generalized Hough transform
abstract
A 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
CVPR1