Demo Data from EDBT 2026 / reviewers in the wild / expert
Pablo Sala
dblp:23/8742
· DBLP profile ↗
4ranked-venue papers
4as first author
0since 2021 · last 2010
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 3 · 3 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 2 first-authorSystems, architecture and hardware · 1 · 1 first-authorApplied, interdisciplinary, general and emerging computing · 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
2 papers |
Segmentation and scene understanding · 64% Robot navigation and mapping · 36% | |
| Computer graphics and multimedia
1 paper |
Image and video processing · 100% | |
| Theoretical computer science
1 paper |
Approximation and online algorithms · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › Segmentation and scene understanding
perceptual grouping |
0.1 | 1 | 2010 | Contour Grouping and Abstraction Using Simple Part Models · ECCV (5) 2010 |
Image and video processing › image segmentation
contour grouping |
0.1 | 1 | 2010 | Contour Grouping and Abstraction Using Simple Part Models · ECCV (5) 2010 |
Robotics › Robot navigation and mapping
visual navigation |
0.1 | 1 | 2006 | Landmark Selection for Vision-Based Navigation · IEEE Trans. Robotics 2006 |
Approximation and online algorithms
approximation algorithms |
0.1 | 1 | 2006 | Landmark Selection for Vision-Based Navigation · IEEE Trans. Robotics 2006 |
Methods — techniques the papers use, named apart from their topics
part-based model · 0.2graph-theoretic formulation · 0.1NP-completeness proof · 0.1
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | Spatiotemporal Contour Grouping Using Abstract Part Models
Pablo Sala, Diego Macrini, Sven J. Dickinson |
ACCV (4) | 1 |
| 2010 | Contour Grouping and Abstraction Using Simple Part Models
Pablo Sala, Sven J. Dickinson |
ECCV (5) | 1 |
| 2006 | Landmark Selection for Vision-Based NavigationabstractRecent work in the object recognition community has yielded a class of interest-point-based features that are stable under significant changes in scale, viewpoint, and illumination, making them ideally suited to landmark-based navigation. Although many such features may be visible in a given view of the robot's environment, only a few such features are necessary to estimate the robot's position and orientation. In this paper, we address the problem of automatically selecting, from the entire set of features visible in the robot's environment, the minimum (optimal) set by which the robot can navigate its environment. Specifically, we decompose the world into a small number of maximally sized regions, such that at each position in a given region, the same small set of features is visible. We introduce a novel graph theoretic formulation of the problem, and prove that it is NP-complete. Next, we introduce a number of approximation algorithms and evaluate them on both synthetic and real data. Finally, we use the decompositions from the real image data to measure the localization performance versus the undecomposed map Pablo Sala, Robert Sim, Ali Shokoufandeh, Sven J. Dickinson |
IEEE Trans. Robotics | 1 |
| 2004 | Landmark selection for vision-based navigationabstractRecent work in the object recognition community has yielded a class of interest point-based features that are stable under significant changes in scale, viewpoint, and illumination, making them ideally suited to landmark-based navigation. Although many such features may be visible in a given view of the robot's environment, only a few such features are necessary to estimate the robot's position and orientation. In this paper, we address the problem of automatically selecting, from the entire set of features visible in the robot's environment, the minimum (optimal) set by which the robot can navigate its environment. Specifically, we decompose the world into a small number of maximally sized regions such that at each position in a given region, the same small set of features is visible. We introduce a novel graph theoretic formulation of the problem and prove that it is NP-complete. Next, we introduce a number of approximation algorithms and evaluate them on both synthetic and real data. Pablo Sala, Robert Sim, Ali Shokoufandeh, Sven J. Dickinson |
IROS | 1 |