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dc.contributor.authorSigut Saavedra, Marta 
dc.contributor.authorFelipe, Jonatán
dc.contributor.authorAcosta Sánchez, Leopoldo 
dc.contributor.otherIngeniería Informática y de Sistemas
dc.date.accessioned2023-12-14T21:05:18Z
dc.date.available2023-12-14T21:05:18Z
dc.date.issued2022
dc.identifier.urihttp://riull.ull.es/xmlui/handle/915/34799
dc.description.abstractThis paper proposes a novel method for the calibration of a stereo camera system used to reconstruct 3D scenes. An error in the pitch angle of the cameras causes the reconstructed scene to exhibit some distortion with respect to the real scene. To do the calibration procedure, whose purpose is to eliminate or at least minimize said distortion, machine learning techniques have been used, and more specifically, regression algorithms. These algorithms are trained with a large number of vectors of input features with their respective outputs, since, in view of the application of the procedure proposed, it is important that the training set be sufficiently representative of the variety that can occur in a real scene, which includes the different orientations that the pitch angle can take, the error in said angle and the effect that all this has on the reconstruction process. The most efficient regression algorithms for estimating the error in the pitch angle are derived from decision trees and certain neural network configurations. Once estimated, the error can be corrected, thus making the reconstructed scene appear more like the real one. Although the authors base their method on U-V disparity and employ this same technique to completely reconstruct the 3D scene, one of the most interesting features of the method proposed is that it can be applied regardless of the technique used to carry out said reconstruction.en
dc.format.mimetypeapplication/pdf
dc.language.isoen
dc.relation.ispartofseriesSensors 2023, 23, 212
dc.rightsLicencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/deed.es_ES
dc.titleCalibration of a stereoscopic vision system in the presence of errors in pitch angleen
dc.typeinfo:eu-repo/semantics/article
dc.identifier.doi10.3390/s23010212
dc.subject.keywordcalibrationen
dc.subject.keywordmachine learningen
dc.subject.keywordU-V disparityen
dc.subject.keyword3D scenes reconstruction


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Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)
Except where otherwise noted, this item's license is described as Licencia Creative Commons (Reconocimiento-No comercial-Sin obras derivadas 4.0 Internacional)