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Carlo Dal Mutto

dblp:88/9941 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision › range sensing
depth sensing
0.212015
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
0.212015
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
0.112015
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
YearPublicationVenuePosition
2015 Probabilistic ToF and Stereo Data Fusion Based on Mixed Pixels Measurement Models
abstract
This 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