Demo Data from VLDB 2026 / reviewers in the wild / expert
Roberto Marzotto
dblp:91/2229
· DBLP profile ↗
3ranked-venue papers
2as 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 · 2 · 2 first-authorGraphics, computer vision, multimedia, augmented reality and games · 2 · 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.
| Computer graphics and multimedia
1 paper |
Computational photography and imaging · 100% |
Topics — the 2 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational photography and imaging
image stitching |
0.0 | 1 | 2004 | High Resolution Video Mosaicing with Global Alignment · CVPR (1) 2004 |
Computational photography and imaging › image stitching
video mosaicing |
0.0 | 1 | 2004 | High Resolution Video Mosaicing with Global Alignment · CVPR (1) 2004 |
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2010 | A real-time versatile roadway path extraction and tracking on an FPGA platform
Roberto Marzotto, Paul Zoratti, Daniele Bagni, Andrea Colombari, Vittorio Murino |
Comput. Vis. Image Underst. | 1 |
| 2004 | High Resolution Video Mosaicing with Global Alignment
Roberto Marzotto, Andrea Fusiello, Vittorio Murino |
CVPR (1) | 1 |
| 2003 | Mosaic of a video shot with multiple moving objectsabstractIn this paper we describe an application which takes a video shot as input and produces a compact representation composed by a background layer and segmented moving objects. We deal with the problems of global registration, super-resolution mosaicing, objects segmentation and tracking. Global registration is achieved with a graph-based technique that exploits situations when the camera returns to a previously seen area. Objects segmentation is based on motion analysis using a robust statistical model of the background. Tracking is based on blob matching using singular value decomposition. Andrea Fusiello, Michele Aprile, Roberto Marzotto, Vittorio Murino |
ICIP (2) | 3 |