Demo Data from SIGIR 2026 / reviewers in the wild / expert
Yukitoshi Watanabe
dblp:169/4701
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 2015
—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 · 61% Robot navigation and mapping · 30% Autonomous driving · 9% |
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
depth estimation |
0.2 | 1 | 2015 | Time-to-contact from image intensity · CVPR 2015 |
Computer vision › 3D vision › motion estimation › direct motion estimation
photometric motion |
0.2 | 1 | 2015 | Time-to-contact from image intensity · CVPR 2015 |
Robotics › Robot navigation and mapping › visual perception for robotics
time-to-contact estimation |
0.2 | 1 | 2015 | Time-to-contact from image intensity · CVPR 2015 |
Robotics › Autonomous driving
driver assistance |
0.1 | 1 | 2015 | Time-to-contact from image intensity · CVPR 2015 |
Methods — techniques the papers use, named apart from their topics
photometric analysis · 0.2intensity change analysis · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Time-to-contact from image intensityabstractIt is known that time-to-contact toward objects can be estimated just from changes in the object size in camera images, and we do not need any additional information, such as camera parameters and motions. However, the existing methods for measuring the time-to-contact are based on geometric image features, such as corners and edge lines, and thus they cannot be used when there are no geometric features in images. In this paper, we propose a new method for computing the time-to-contact from photometric information in images. When a light source moves in the 3D scene, an observed intensity changes according to the motion of the light source. In this paper, we analyze the change in photometric information in images, and show that the time-to-contact can be estimated just from the changes in intensity in images. Our method does not need any additional information, such as radiance of light source, reflectance of object and orientation of object surface. The proposed method can be used in various applications, such as vehicle driver assistance. Yukitoshi Watanabe, Fumihiko Sakaue, Jun Sato |
CVPR | 1 |