Demo Data from VLDB 2026 / reviewers in the wild / expert
István Szentandrási
dblp:124/0705
· DBLP profile ↗
4ranked-venue papers
2as 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 · 4 · 2 first-authorHuman-computer interaction and ubiquitous computing · 3 · 2 first-authorArtificial intelligence and machine learning · 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.
| Computer graphics and multimedia
3 papers |
Virtual and augmented reality · 90% Computational photography and imaging · 10% | |
| Artificial intelligence
1 paper |
3D vision · 100% |
Topics — the 7 heaviest of 7, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Virtual and augmented reality
augmented reality |
0.5 | 3 | 2015 | INCAST: Interactive Camera Streams for Surveillance Cams AR · ISMAR 2015 Poor man's SimulCam: Real-time and effortless matchmoving · ISMAR 2013 Uniform Marker Fields: Camera localization by orientable De Bruijn tori · ISMAR 2012 |
Virtual and augmented reality › tracking
camera pose estimation |
0.4 | 3 | 2015 | INCAST: Interactive Camera Streams for Surveillance Cams AR · ISMAR 2015 Poor man's SimulCam: Real-time and effortless matchmoving · ISMAR 2013 Uniform Marker Fields: Camera localization by orientable De Bruijn tori · ISMAR 2012 |
Computer vision › 3D vision
camera pose estimation |
0.2 | 1 | 2013 | Five Shades of Grey for Fast and Reliable Camera Pose Estimation · CVPR 2013 |
Computer vision › 3D vision › pose estimation › correspondence-based pose estimation
marker-based pose estimation |
0.2 | 1 | 2013 | Five Shades of Grey for Fast and Reliable Camera Pose Estimation · CVPR 2013 |
Virtual and augmented reality › tracking
marker-based tracking |
0.2 | 1 | 2013 | Poor man's SimulCam: Real-time and effortless matchmoving · ISMAR 2013 |
Computational photography and imaging
camera localization |
0.1 | 1 | 2012 | Uniform Marker Fields: Camera localization by orientable De Bruijn tori · ISMAR 2012 |
Virtual and augmented reality
fiducial markers |
0.1 | 1 | 2012 | Uniform Marker Fields: Camera localization by orientable De Bruijn tori · ISMAR 2012 |
Methods — techniques the papers use, named apart from their topics
vanishing point detection · 0.2planar uniform marker fields · 0.2planar grid detection · 0.2de bruijn torus · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | INCAST: Interactive Camera Streams for Surveillance Cams ARabstractAugmented reality does not make any sense for fixed cameras. Or does it? In this work, we are dealing with static cameras and their usability for interactive augmented reality applications. Knowing that the camera does not move makes camera pose estimation both less and more difficult — one does not have to deal with pose change in time, but on the other hand, obtaining some level of understanding of the scene from a single viewpoint is challenging. We propose several ways how to gain advantage from the camera being static and a pipeline of a system for broadcasting a video stream enriched by information needed for its interactive visual augmenting — Interactive Camera Streams, INCAST. We present a proof-of-concept system showing the usability of INCASt on several use-cases — non-interactive demos and simple AR games. István Szentandrási, Michael Zachariá, Rudolf Kajan, Jan Tinka, Markéta Dubská, Jakub Sochor, Adam Herout |
ISMAR | 1 |
| 2013 | Five Shades of Grey for Fast and Reliable Camera Pose EstimationabstractWe introduce here an improved design of the Uniform Marker Fields and an algorithm for their fast and reliable detection. Our concept of the marker field is designed so that it can be detected and recognized for camera pose estimation: in various lighting conditions, under a severe perspective, while heavily occluded, and under a strong motion blur. Our marker field detection harnesses the fact that the edges within the marker field meet at two vanishing points and that the projected planar grid of squares can be defined by a detectable mathematical formalism. The modules of the grid are grey scale and the locations within the marker field are defined by the edges between the modules. The assumption that the marker field is planar allows for a very cheap and reliable camera pose estimation in the captured scene. The detection rates and accuracy are slightly better compared to state-of-the-art marker-based solutions. At the same time, and more importantly, our detector of the marker field is several times faster and the reliable real-time detection can be thus achieved on mobile and low-power devices. We show three targeted applications where the planarity is assured and where the presented marker field design and detection algorithm provide a reliable and extremely fast solution. Adam Herout, István Szentandrási, Michal Zachariás, Markéta Dubská, Rudolf Kajan |
CVPR | 2 |
| 2013 | Poor man's SimulCam: Real-time and effortless matchmovingabstractIn this article, we propose an instant matchmoving solution for green screen. It uses a recent technique of planar uniform marker fields. Marker fields are an extension of planar markers used in augmented reality, offering better reliability and performance suitable for our task: tolerance to occlusion, speed of detection, and use of arbitrary low-contrast colors. We show that marker fields of shades of green (or blue or other color) can be used to obtain an instant and effortless camera pose estimation. We provide exemplar applications of the presented technique: virtual camera/simulcam and live storyboarding or shot prototyping. The matchmoving technique based on marker fields of shades of green is very computationally efficient - our measurements show that the matchmoving preview and living storyboard editing and recording can be easily done on today's ultramobile devices. Our technique is thus available to anyone at low cost and with easy setup, opening space for new levels of filmmakers' creative expression. Markéta Dubská, István Szentandrási, Michal Zachariás, Adam Herout |
ISMAR | 2 |
| 2012 | Uniform Marker Fields: Camera localization by orientable De Bruijn toriabstractIn various applications, a wider area needs to be covered by fiduciary markers but a large marker cannot be used because only a fraction of the area is to be viewed by the camera. Such an area can be covered by a number of small markers with unique identifiers. However, with the camera freely moving in the scene and with occluders present, it is difficult to ensure that at least one of the individual markers is completely visible, unless the markers are small and numerous. In that case, the markers are not recognizable from larger distances. István Szentandrási, Michal Zachariás, Jirí Havel, Adam Herout, Markéta Dubská, Rudolf Kajan |
ISMAR | 1 |