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Demetrios Betsis

dblp:86/1421 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1995
—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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
camera calibration
0.011995
Head-Eye Calibration · ICCV 1995
Computer vision › 3D vision › camera calibration
head-eye calibration
0.011995
Head-Eye Calibration · ICCV 1995
Computer vision › 3D vision › camera calibration
active calibration
0.011995
Head-Eye Calibration · ICCV 1995

Methods — techniques the papers use, named apart from their topics

nonlinear optimization · 0.0closed-form solution · 0.0
YearPublicationVenuePosition
1995 Head-Eye Calibration
abstract
We deal with the calibration problem of an active head-eye system, which consists of a pair of cameras mounted on a head with 13 degrees of freedom. The aim of the calibration is to establish relative positions of different 3D systems: between camera and neck, eye and neck, etc., so that we can keep track of the camera position in a fixed (calibration) reference system as a function of the visual parameters of the head-eye system. We formulate the problem and propose both closed-form and nonlinear optimization approaches to solve it. Experiments were carried out and comparison of results with other algorithms were made on both simulated and real data.>
Mengxiang Li, Demetrios Betsis
ICCV2