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ACCEPTED FOR PUBLICATION ON IEEE TRANSACTION ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 Probabilistic ToF and Stereo Data Fusion Based on Mixed Pixels Measurement Models
BibTeX
@MISC{Mutto_acceptedfor,
author = {Carlo Dal Mutto and Pietro Zanuttigh and Guido Maria Cortelazzo and Senior Member},
title = {ACCEPTED FOR PUBLICATION ON IEEE TRANSACTION ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1 Probabilistic ToF and Stereo Data Fusion Based on Mixed Pixels Measurement Models},
year = {}
}
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Abstract
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.
