Demo Data from SIGIR 2026 / reviewers in the wild / expert
David Jelinek
dblp:93/6750
· DBLP profile ↗
3ranked-venue papers
3as first author
0since 2021 · last 2002
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 3 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 2 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
2 papers |
Computational photography and imaging · 43% Rendering · 31% Geometric modeling and processing · 27% | |
| Artificial intelligence
2 papers |
3D vision · 100% |
Topics — the 8 heaviest of 8, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision › 3d scene understanding
occlusion reasoning |
0.0 | 1 | 2002 | View Synthesis with Occlusion Reasoning Using Quasi-Sparse Feature Correspondences · ECCV (2) 2002 |
Rendering
novel view synthesis |
0.0 | 1 | 2002 | View Synthesis with Occlusion Reasoning Using Quasi-Sparse Feature Correspondences · ECCV (2) 2002 |
Geometric modeling and processing
3d reconstruction |
0.0 | 1 | 2001 | Reconstruction of Linearly Parameterized Models from Single Images with a Camera of Unknown Focal Length · IEEE Trans. Pattern Anal. Mach. Intell. 2001 |
Computational photography and imaging › image-based modeling › 3d reconstruction from images
single-view 3d reconstruction |
0.0 | 1 | 2001 | Reconstruction of Linearly Parameterized Models from Single Images with a Camera of Unknown Focal Length · IEEE Trans. Pattern Anal. Mach. Intell. 2001 |
Computer vision › 3D vision
camera pose estimation |
0.0 | 1 | 1999 | Reconstruction of Linearly Parameterized Models from Single Images with a Camera of Unknown Focal Length · CVPR 1999 |
Computational photography and imaging
camera calibration |
0.0 | 1 | 2001 | Reconstruction of Linearly Parameterized Models from Single Images with a Camera of Unknown Focal Length · IEEE Trans. Pattern Anal. Mach. Intell. 2001 |
Computational photography and imaging › camera calibration
unknown focal length |
0.0 | 1 | 2001 | Reconstruction of Linearly Parameterized Models from Single Images with a Camera of Unknown Focal Length · IEEE Trans. Pattern Anal. Mach. Intell. 2001 |
Computer vision › 3D vision
camera calibration |
0.0 | 1 | 1999 | Reconstruction of Linearly Parameterized Models from Single Images with a Camera of Unknown Focal Length · CVPR 1999 |
Methods — techniques the papers use, named apart from their topics
quasi-sparse feature correspondences · 0.1nonlinear optimization · 0.0multistart method · 0.0unconstrained optimization · 0.0feature correspondence · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2002 | View Synthesis with Occlusion Reasoning Using Quasi-Sparse Feature Correspondences
David Jelinek, Camillo J. Taylor |
ECCV (2) | 1 |
| 2001 | Reconstruction of Linearly Parameterized Models from Single Images with a Camera of Unknown Focal LengthabstractThis paper deals with the problem of recovering the dimensions of an object and its pose from a single image acquired with a camera of unknown focal length. It is assumed that the object in question can be modeled as a polyhedron where the coordinates of the vertices can be expressed as a linear function of a dimension vector. The reconstruction program takes as input, a set of correspondences between features in the model and features in the image. From this information, the program determines an appropriate projection model for the camera, the dimensions of the object, its pose relative to the camera and, in the case of perspective projection, the focal length of the camera. This paper describes how the reconstruction problem can be framed as an optimization over a compact set with low dimension (no more than four). This optimization problem can be solved efficiently by coupling standard nonlinear optimization techniques with a multistart method. The result is an efficient, reliable solution system that does not require initial estimates for any of the parameters being estimated. David Jelinek, Camillo J. Taylor |
IEEE Trans. Pattern Anal. Mach. Intell. | 1 |
| 1999 | Reconstruction of Linearly Parameterized Models from Single Images with a Camera of Unknown Focal LengthabstractThis paper deals with the problem of recovering the dimensions of an object and its pose from a single image acquired with a camera of unknown focal length. It is assumed that the object in question can be modeled as a polyhedron where the coordinates of the vertices can be expressed as a linear function of a dimension vector, /spl lambda/. The reconstruction program takes as input a set of correspondences between features in the model and features in the image. From this information the program determines an appropriate projection model for the camera (scaled orthographic or perspective), the dimensions of the object, its pose relative to the camera and, in the case of perspective projection, the focal length of the camera. We demonstrate that this reconstruction task can be framed as an unconstrained optimization problem involving a small number of variables, no more than four, regardless of the number of parameters in the dimension vector. David Jelinek, Camillo J. Taylor |
CVPR | 1 |