Demo Data from SIGIR 2026 / reviewers in the wild / expert
Kevin Merckx
dblp:170/8936
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1Human-computer interaction and ubiquitous 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.
| Human-computer interaction and pervasive computing
1 paper |
Interaction techniques and input · 87% Ubiquitous computing and smart environments · 13% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Interaction techniques and input
gesture input |
0.2 | 1 | 2015 | Natural User Interface for Ambient Objects · ISMAR 2015 |
Interaction techniques and input › target selection
pointing |
0.2 | 1 | 2015 | Natural User Interface for Ambient Objects · ISMAR 2015 |
Ubiquitous computing and smart environments › ambient intelligence
ambient interaction |
0.1 | 1 | 2015 | Natural User Interface for Ambient Objects · ISMAR 2015 |
Methods — techniques the papers use, named apart from their topics
calibration · 0.2RGB-D sensing · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Natural User Interface for Ambient ObjectsabstractTo help the computing device always understand the spacial relationship between the user's gesture and the ambient objects, a methodology is proposed to find the user's virtual eye center in the wearable camera coordinate system and then calculate accurately where a user is pointing at to perform the natural interaction. First, the wearable RGB-D sensor is affixed around the user forehead. A tool-free calibration is done by having the user move their fingers along their lines of sight from his eye center to the random selected targets. The fingertips are detected in the depth camera and then the interaction of these lines of sight is calculated. Then we present how to find where the user is pointing at in different scenarios with a depth map, a detected object and a controlled virtual element. To validate our methods, we perform a point-to-screen experiment. Results demonstrate that when a user is interacting with a display up to 1.5 meters away, our natural gesture interface has an average error of 2.1cm. In conclusion, the presented technique is a viable option for a reliable user interaction. Kevin Merckx, Pascal Fallavollita, Nassir Navab |
ISMAR | 2 |