Demo Data from SIGIR 2026 / reviewers in the wild / expert
Carlo Dal Mutto
dblp:88/9941
· 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-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 · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision › range sensing
depth sensing |
0.2 | 1 | 2015 | Probabilistic ToF and Stereo Data Fusion Based on Mixed Pixels Measurement Models · IEEE Trans. Pattern Anal. Mach. Intell. 2015 |
Computer vision › 3D vision › depth estimation › multimodal depth estimation
time-of-flight and stereo fusion |
0.2 | 1 | 2015 | Probabilistic ToF and Stereo Data Fusion Based on Mixed Pixels Measurement Models · IEEE Trans. Pattern Anal. Mach. Intell. 2015 |
Computer vision › 3D vision
depth estimation |
0.1 | 1 | 2015 | Probabilistic ToF and Stereo Data Fusion Based on Mixed Pixels Measurement Models · IEEE Trans. Pattern Anal. Mach. Intell. 2015 |
Methods — techniques the papers use, named apart from their topics
maximum likelihood · 0.2loopy belief propagation · 0.2MAP-MRF · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | Probabilistic ToF and Stereo Data Fusion Based on Mixed Pixels Measurement ModelsabstractThis paper proposes a method for fusing data acquired by a ToF camera and a stereo pair based on a model for depth measurement by ToF cameras which accounts also for depth discontinuity artifacts due to the mixed pixel effect. Such model is exploited within both a ML and a MAP-MRF frameworks for ToF and stereo data fusion. The proposed MAP-MRF framework is characterized by site-dependent range values, a rather important feature since it can be used both to improve the accuracy and to decrease the computational complexity of standard MAP-MRF approaches. This paper, in order to optimize the site dependent global cost function characteristic of the proposed MAP-MRF approach, also introduces an extension to Loopy Belief Propagation which can be used in other contexts. Experimental data validate the proposed ToF measurements model and the effectiveness of the proposed fusion techniques. Carlo Dal Mutto, Pietro Zanuttigh, Guido M. Cortelazzo |
IEEE Trans. Pattern Anal. Mach. Intell. | 1 |