Demo Data from SIGIR 2026 / reviewers in the wild / expert
Jeffrey Mendelsohn
dblp:55/4090
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Virtual and augmented reality › tracking
camera pose estimation |
0.0 | 1 | 1999 | Constrained Self-Calibration · CVPR 1999 |
Computational photography and imaging › camera calibration
camera self-calibration |
0.0 | 1 | 1999 | Constrained Self-Calibration · CVPR 1999 |
Virtual and augmented reality
augmented reality |
0.0 | 1 | 1999 | Constrained Self-Calibration · CVPR 1999 |
Methods — techniques the papers use, named apart from their topics
target-based calibration · 0.0bundle adjustment · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1999 | Constrained Self-CalibrationabstractThis 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 |
CVPR | 1 |
| 1997 | Discrete-Time Rigidity-Constrained Optical Flow
Jeffrey Mendelsohn, Eero P. Simoncelli, Ruzena Bajcsy |
CAIP | 1 |