Demo Data from SIGIR 2026 / reviewers in the wild / expert
Jana Kostlivá
dblp:92/2436
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2007
—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 · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
Performance modeling and evaluation · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision › stereo vision › stereo matching
dense stereo matching |
0.1 | 1 | 2007 | Feasibility Boundary in Dense and Semi-Dense Stereo Matching · CVPR 2007 |
Computer vision › 3D vision › stereo vision › stereo matching
semi-dense stereo |
0.1 | 1 | 2007 | Feasibility Boundary in Dense and Semi-Dense Stereo Matching · CVPR 2007 |
Computer vision › 3D vision › stereo vision
stereo matching |
0.1 | 1 | 2007 | Feasibility Boundary in Dense and Semi-Dense Stereo Matching · CVPR 2007 |
Performance modeling and evaluation
benchmarking |
0.0 | 1 | 2007 | Feasibility Boundary in Dense and Semi-Dense Stereo Matching · CVPR 2007 |
Methods — techniques the papers use, named apart from their topics
receiver operating characteristic analysis · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2007 | Feasibility Boundary in Dense and Semi-Dense Stereo MatchingabstractIn stereo literature, there is no standard method for evaluating algorithms for semi-dense stereo matching. Moreover, existing evaluations for dense methods require a fixed parameter setting for the tested algorithms. In this paper, we propose a method that overcomes these drawbacks and still is able to compare algorithms based on a simple numerical value, so that reporting results does not take up much space in a paper. We propose evaluation of stereo algorithms based on Receiver Operating Characteristics (ROC) which captures both errors and sparsity. By comparing ROC curves of all tested algorithms we obtain the Feasibility Boundary, the best possible performance achieved by a set of tested stereo algorithms, which allows stereo algorithm users to select the proper method and parameter setting for a required application. Jana Kostlivá, Jan Cech, Radim Sára |
CVPR | 1 |