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Authors: Frithjof Gressmann ; Timo Lüddecke ; Tatyana Ivanovska ; Markus Schoeler and Florentin Wörgötter

Affiliation: Georg-August-University, Germany

ISBN: 978-989-758-226-4

Keyword(s): Object Recognition, Part Perception, Deep Learning, Neural Networks, 3D Objects.

Related Ontology Subjects/Areas/Topics: Applications ; Computer Vision, Visualization and Computer Graphics ; Image and Video Analysis ; Pattern Recognition ; Robotics ; Segmentation and Grouping ; Software Engineering

Abstract: During the last years, approaches based on convolutional neural networks (CNN) had substantial success in visual object perception. CNNs turned out to be capable of extracting high-level features of objects, which allow for fine-grained classification. However, some object classes exhibit tremendous variance with respect to their instances appearance. We believe that considering object parts as an intermediate representation could be helpful in these cases. In this work, a part-driven perception of everyday objects with a rotation estimation is implemented using deep convolution neural networks. The used network is trained and tested on artificially generated RGB-D data. The approach has a potential to be used for part recognition of realistic sensor recordings in present robot systems.

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Paper citation in several formats:
Gressmann, F.; Lüddecke, T.; Ivanovska, T.; Schoeler, M. and Wörgötter, F. (2017). Part-driven Visual Perception of 3D Objects.In Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP, (VISIGRAPP 2017) ISBN 978-989-758-226-4, pages 370-377. DOI: 10.5220/0006211203700377

@conference{visapp17,
author={Frithjof Gressmann. and Timo Lüddecke. and Tatyana Ivanovska. and Markus Schoeler. and Florentin Wörgötter.},
title={Part-driven Visual Perception of 3D Objects},
booktitle={Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP, (VISIGRAPP 2017)},
year={2017},
pages={370-377},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006211203700377},
isbn={978-989-758-226-4},
}

TY - CONF

JO - Proceedings of the 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5 VISAPP: VISAPP, (VISIGRAPP 2017)
TI - Part-driven Visual Perception of 3D Objects
SN - 978-989-758-226-4
AU - Gressmann, F.
AU - Lüddecke, T.
AU - Ivanovska, T.
AU - Schoeler, M.
AU - Wörgötter, F.
PY - 2017
SP - 370
EP - 377
DO - 10.5220/0006211203700377

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