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Jeffrey Mendelsohn

dblp:55/4090 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 1999
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Graphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-authorArtificial intelligence and machine learning · 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.

Computer graphics and multimedia
1 paper
Virtual and augmented reality · 56% Computational photography and imaging · 44%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Virtual and augmented reality › tracking
camera pose estimation
0.011999
Constrained Self-Calibration · CVPR 1999
Computational photography and imaging › camera calibration
camera self-calibration
0.011999
Constrained Self-Calibration · CVPR 1999
Virtual and augmented reality
augmented reality
0.011999
Constrained Self-Calibration · CVPR 1999

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

target-based calibration · 0.0bundle adjustment · 0.0
YearPublicationVenuePosition
1999 Constrained Self-Calibration
abstract
This paper focuses on the estimation of the intrinsic camera parameters and the trajectory of the camera from an image sequence. Intrinsic camera calibration and pose estimation are the prerequisites for many applications involving navigation tasks, scene reconstruction, and merging of virtual and real environments. Proposed and evaluated is a technical solution to decrease the sensitivity of self-calibration by placing easily identifiable targets of known shape in the environment. The relative position of the targets need not be known a priori. Assuming an appropriate ratio of size to distance these targets resolve known ambiguities. Constraints on the target placement and the cameras' motions are explored. The algorithm is extensively tested in a variety of real-world scenarios.
Jeffrey Mendelsohn, Kostas Daniilidis
CVPR1
1997 Discrete-Time Rigidity-Constrained Optical Flow
Jeffrey Mendelsohn, Eero P. Simoncelli, Ruzena Bajcsy
CAIP1