A study of the effect of noise and occlusion on the accuracy of convolutional neural networks applied to 3D object recognition

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/72633
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dc.contributorInformática Industrial y Redes de Computadoreses_ES
dc.contributorRobótica y Visión Tridimensional (RoViT)es_ES
dc.contributor.authorGarcia-Garcia, Alberto-
dc.contributor.authorGarcia-Rodriguez, Jose-
dc.contributor.authorOrts-Escolano, Sergio-
dc.contributor.authorOprea, Sergiu-
dc.contributor.authorGomez-Donoso, Francisco-
dc.contributor.authorCazorla, Miguel-
dc.contributor.otherUniversidad de Alicante. Departamento de Tecnología Informática y Computaciónes_ES
dc.contributor.otherUniversidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificiales_ES
dc.contributor.otherUniversidad de Alicante. Instituto Universitario de Investigación Informáticaes_ES
dc.date.accessioned2018-01-19T10:28:57Z-
dc.date.available2018-01-19T10:28:57Z-
dc.date.issued2017-11-
dc.identifier.citationComputer Vision and Image Understanding. 2017, 164: 124-134. doi:10.1016/j.cviu.2017.06.006es_ES
dc.identifier.issn1077-3142 (Print)-
dc.identifier.issn1090-235X (Online)-
dc.identifier.urihttp://hdl.handle.net/10045/72633-
dc.description.abstractIn this work, we carry out a study of the effect of adverse conditions, which characterize real-world scenes, on the accuracy of a Convolutional Neural Network applied to 3D object class recognition. Firstly, we discuss possible ways of representing 3D data to feed the network. In addition, we propose a set of representations to be tested. Those representations consist of a grid-like structure (fixed and adaptive) and a measure for the occupancy of each cell of the grid (binary and normalized point density). After that, we propose and implement a Convolutional Neural Network for 3D object recognition using Caffe. At last, we carry out an in-depth study of the performance of the network over a 3D CAD model dataset, the Princeton ModelNet project, synthetically simulating occlusions and noise models featured by common RGB-D sensors. The results show that the volumetric representations for 3D data play a key role on the recognition process and Convolutional Neural Network can be considerably robust to noise and occlusions if a proper representation is chosen.es_ES
dc.description.sponsorshipThis work has been supported by the Spanish Government DPI2013-40534-R grant for the SIRMAVED project, also supported with FEDER funds. This work has also been funded by the grant “Ayudas para Estudios de Máster e Iniciación a la Investigación” from the University of Alicante.es_ES
dc.languageenges_ES
dc.publisherElsevieres_ES
dc.rights© 2017 Elsevier Inc.es_ES
dc.subjectDeep learninges_ES
dc.subject3D object recognitiones_ES
dc.subjectConvolutional neural networkses_ES
dc.subjectNoisees_ES
dc.subjectOcclusiones_ES
dc.subjectCaffees_ES
dc.subject.otherArquitectura y Tecnología de Computadoreses_ES
dc.subject.otherCiencia de la Computación e Inteligencia Artificiales_ES
dc.titleA study of the effect of noise and occlusion on the accuracy of convolutional neural networks applied to 3D object recognitiones_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.peerreviewedsies_ES
dc.identifier.doi10.1016/j.cviu.2017.06.006-
dc.relation.publisherversionhttp://dx.doi.org/10.1016/j.cviu.2017.06.006es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
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INV - RoViT - Artículos de Revistas

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