Demo Data from SIGIR 2026 / reviewers in the wild / expert
Jean-Christophe Robert
dblp:139/7188
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1991
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Applied, interdisciplinary, general and emerging computing · 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 |
Robot navigation and mapping · 77% 3D vision · 23% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Robotics › Robot navigation and mapping
sensor modeling |
0.0 | 1 | 1991 | Modeling sensor detectability with the VANTAGE geometric/sensor modeler · IEEE Trans. Robotics Autom. 1991 |
Computer vision › 3D vision › object modeling
geometric modeling |
0.0 | 1 | 1991 | Modeling sensor detectability with the VANTAGE geometric/sensor modeler · IEEE Trans. Robotics Autom. 1991 |
Methods — techniques the papers use, named apart from their topics
set operations · 0.0sensor-composition tree · 0.0g-source illumination conditions · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1991 | Modeling sensor detectability with the VANTAGE geometric/sensor modelerabstractA G-source, an abstract sensor component representing either a light source or a TV camera, is defined. G-source illumination conditions are investigated to describe the condition under which each G-source illuminates (or observes) surface regions with respect to its illumination/observation directions. Sensor detectability is defined as a function that takes object boundaries and produces a set of points on the object from which the sensor can obtain useful data. It is shown that sensor detectability can be represented by G-source illumination conditions and/or operations between them. The way in which G-sources are combined is described by a sensor-composition tree in which leaf nodes represent G-source illumination conditions and branch nodes represent set operations. Applications of the sensor composition tree to object boundaries are investigated and a set of detectable points is obtained using the VANTAGE geometric modeler. Several 2D structures are proposed to represent these detected points given by VANTAGE. A brief overview of how to use these capabilities in building model-based vision systems is given.> Katsushi Ikeuchi, Jean-Christophe Robert |
IEEE Trans. Robotics Autom. | 2 |