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Authors: Viktor Seib ; Norman Link and Dietrich Paulus

Affiliation: University of Koblenz-Landau, Germany

Keyword(s): Implicit Shape Models, 3D Shape Classification, Object Recognition, Hough-Transform.

Abstract: Recently, different adaptations of Implicit Shape Models (ISM) for 3D shape classification have been presented. In this paper we propose a new method with a continuous voting space and keypoint extraction by uniform sampling. We evaluate different sets of typical parameters involved in the ISM algorithm and compare the proposed algorithm on a large public dataset with state of the art approaches.

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Paper citation in several formats:
Seib, V.; Link, N. and Paulus, D. (2015). Implicit Shape Models for 3D Shape Classification with a Continuous Voting Space. In Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 3: VISAPP; ISBN 978-989-758-090-1; ISSN 2184-4321, SciTePress, pages 33-43. DOI: 10.5220/0005290700330043

@conference{visapp15,
author={Viktor Seib. and Norman Link. and Dietrich Paulus.},
title={Implicit Shape Models for 3D Shape Classification with a Continuous Voting Space},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 3: VISAPP},
year={2015},
pages={33-43},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005290700330043},
isbn={978-989-758-090-1},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications (VISIGRAPP 2015) - Volume 3: VISAPP
TI - Implicit Shape Models for 3D Shape Classification with a Continuous Voting Space
SN - 978-989-758-090-1
IS - 2184-4321
AU - Seib, V.
AU - Link, N.
AU - Paulus, D.
PY - 2015
SP - 33
EP - 43
DO - 10.5220/0005290700330043
PB - SciTePress