Mixing deep learning with classical vision for object recognition

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dc.contributor.authorStefańczyk, Maciej
dc.contributor.authorBocheński, Tomasz
dc.contributor.editorSkala, Václav
dc.date.accessioned2020-07-24T07:58:15Z
dc.date.available2020-07-24T07:58:15Z
dc.date.issued2020
dc.description.abstract-translatedNowadays, when one needs a system for image recognition, it is mostly a matter of finding pre-trained CNN and, sometimes, adding additional training based on transferred knowledge. Accurate 6-DOF object localization in the image is a more laborious task and requires more complex training data to be available. On the other hand, if we know the model of the object, it is straightforward to acquire its pose from the image (RGB or RGB-D). In this paper, we try to show the advantages of mixing deep learning object recognition/detection with classical 6-DOF pose estimation algorithms, with a focus on applications in service robotics.en
dc.format8 s.cs
dc.format.mimetypeapplication/pdf
dc.identifier.citationJournal of WSCG. 2020, vol. 28, no. 1-2, p. 147-154.en
dc.identifier.doihttps://doi.org/10.24132/JWSCG.2020.28.18
dc.identifier.issn1213-6972 (print)
dc.identifier.issn1213-6980 (CD-ROM)
dc.identifier.issn1213-6964 (on-line)
dc.identifier.urihttp://wscg.zcu.cz/WSCG2020/2020-J_WSCG-1-2.pdf
dc.identifier.urihttp://hdl.handle.net/11025/38436
dc.language.isoenen
dc.publisherVáclav Skala - UNION Agencycs
dc.relation.ispartofseriesJournal of WSCGen
dc.rights© Václav Skala - UNION Agencycs
dc.rights.accessopenAccessen
dc.subjectdetekce objektů CNNcs
dc.subjectVGG16cs
dc.subjectResNet50cs
dc.subjectodhad 6 DoF pózycs
dc.subjectRanSaCcs
dc.subjectICPcs
dc.subjectRGB-Dcs
dc.subject.translatedCNN object detectionen
dc.subject.translatedVGG16en
dc.subject.translatedResNet50en
dc.subject.translated6-DOF pose estimationen
dc.subject.translatedRanSaCen
dc.subject.translatedICPen
dc.subject.translatedRGB-Den
dc.titleMixing deep learning with classical vision for object recognitionen
dc.typečlánekcs
dc.typearticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen

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Volume 28, Number 1-2 (2020)