loading
Papers

Research.Publish.Connect.

Paper

Paper Unlock

Authors: Viktor Seib ; Norman Link and Dietrich Paulus

Affiliation: University of Koblenz-Landau, Germany

ISBN: 978-989-758-090-1

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.

PDF ImageFull Text

Download
CC BY-NC-ND 4.0

Sign In Guest: Register as new SciTePress user now for free.

Sign In SciTePress user: please login.

PDF ImageMy Papers

You are not signed in, therefore limits apply to your IP address 35.171.45.91

In the current month:
Recent papers: 100 available of 100 total
2+ years older papers: 200 available of 200 total

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 - Volume 3: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-090-1, 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 - Volume 3: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={33-43},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005290700330043},
isbn={978-989-758-090-1},
}

TY - CONF

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

Login or register to post comments.

Comments on this Paper: Be the first to review this paper.