Název:	Mixing deep learning with classical vision for object recognition
Autoři:	Stefańczyk, Maciej Bocheński, Tomasz
Citace zdrojového dokumentu:	Journal of WSCG. 2020, vol. 28, no. 1-2, p. 147-154.
Datum vydání:	2020
Nakladatel:	Václav Skala - UNION Agency
Typ dokumentu:	článek article
URI:	http://wscg.zcu.cz/WSCG2020/2020-J_WSCG-1-2.pdf http://hdl.handle.net/11025/38436
ISSN:	1213-6972 (print) 1213-6980 (CD-ROM) 1213-6964 (on-line)
Klíčová slova:	detekce objektů CNN;VGG16;ResNet50;odhad 6 DoF pózy;RanSaC;ICP;RGB-D
Klíčová slova v dalším jazyce:	CNN object detection;VGG16;ResNet50;6-DOF pose estimation;RanSaC;ICP;RGB-D
Abstrakt v dalším jazyce:	Nowadays, 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.
Práva:	© Václav Skala - UNION Agency
Vyskytuje se v kolekcích:	Volume 28, Number 1-2 (2020)

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