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dc.contributor.authorHahne, Christopher
dc.contributor.authorAggoun, Amar
dc.contributor.authorVelisavljevic, Vladan
dc.contributor.authorFiebig, Susanne
dc.contributor.authorPesch, Matthias
dc.date.accessioned2017-08-25T10:42:39Z
dc.date.available2017-08-25T10:42:39Z
dc.date.issued2017-08-20
dc.identifier.citationHahne, C., Aggoun, A., Velisavljevic, V., Fiebig, S., Pesch, M., (2017) 'Baseline and Triangulation Geometry in a Standard Plenoptic Camera', International Journal of Computer Vision. 126 (1) pp. 21–35 doi :10.1007/s11263-017-1036-4
dc.identifier.issn0920-5691
dc.identifier.doi10.1007/s11263-017-1036-4
dc.identifier.urihttp://hdl.handle.net/2436/620617
dc.description© 2017 The Authors. Published by Elsevier. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1007/s11263-017-1036-4
dc.description.abstractIn this paper, we demonstrate light field triangulation to determine depth distances and baselines in a plenoptic camera. Advances in micro lenses and image sensors have enabled plenoptic cameras to capture a scene from different viewpoints with sufficient spatial resolution. While object distances can be inferred from disparities in a stereo viewpoint pair using triangulation, this concept remains ambiguous when applied in the case of plenoptic cameras. We present a geometrical light field model allowing the triangulation to be applied to a plenoptic camera in order to predict object distances or specify baselines as desired. It is shown that distance estimates from our novel method match those of real objects placed in front of the camera. Additional benchmark tests with an optical design software further validate the model’s accuracy with deviations of less than ±0.33% for several main lens types and focus settings. A variety of applications in the automotive and robotics field can benefit from this estimation model.
dc.description.sponsorshipThis research was supported in part by the EU as Project 3D VIVANT under EU-FP7 ICT-2010-248420.
dc.language.isoen
dc.publisherSpringer
dc.relation.urlhttp://link.springer.com/10.1007/s11263-017-1036-4
dc.subjectLight Field
dc.subjectPlenoptic
dc.subjectCamera
dc.subjectMicroscope
dc.subjectTriangulation
dc.subjectBaseline
dc.subjectDistance
dc.subjectEstimation
dc.titleBaseline and triangulation geometry in a standard plenoptic camera
dc.typeJournal article
dc.identifier.journalInternational Journal of Computer Vision
dc.date.accepted2017-07-20
rioxxterms.funderUniversity of Wolverhampton
rioxxterms.identifier.projectUoW250817AA
rioxxterms.versionVoR
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
rioxxterms.licenseref.startdate2017-08-25
dc.source.volume126
dc.source.beginpage21
dc.source.endpage35
refterms.dateFCD2018-10-19T09:24:44Z
refterms.versionFCDVoR
refterms.dateFOA2018-08-20T00:00:00Z
html.description.abstractIn this paper, we demonstrate light field triangulation to determine depth distances and baselines in a plenoptic camera. Advances in micro lenses and image sensors have enabled plenoptic cameras to capture a scene from different viewpoints with sufficient spatial resolution. While object distances can be inferred from disparities in a stereo viewpoint pair using triangulation, this concept remains ambiguous when applied in the case of plenoptic cameras. We present a geometrical light field model allowing the triangulation to be applied to a plenoptic camera in order to predict object distances or specify baselines as desired. It is shown that distance estimates from our novel method match those of real objects placed in front of the camera. Additional benchmark tests with an optical design software further validate the model’s accuracy with deviations of less than ±0.33% for several main lens types and focus settings. A variety of applications in the automotive and robotics field can benefit from this estimation model.


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