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Authors: Hannes Kisner and Ulrike Thomas

Affiliation: Chemnitz University of Technology, Germany

Keyword(s): Spectral Clustering, Segmentation, Graph Laplacian, Point Clouds.

Related Ontology Subjects/Areas/Topics: Color and Texture Analyses ; Computer Vision, Visualization and Computer Graphics ; Features Extraction ; Image and Video Analysis ; Segmentation and Grouping

Abstract: For many applications like pose estimation it is important to obtain good segmentation results as a pre-processing step. Spectral clustering is an efficient method to achieve high quality results without a priori knowledge about the scene. Among other methods, it is either the k-means based spectral clustering approach or the bi-spectral clustering approach, which are suitable for 3D point clouds. In this paper, a new method is introduced and the results are compared to these well-known spectral clustering algorithms. When implementing the spectral clustering methods key issues are: how to define similarity, how to build the graph Laplacian and how to choose the number of clusters without any or less a-priori knowledge. The suggested spectral clustering approach is described and evaluated with 3D point clouds. The advantage of this approach is that no a-priori knowledge about the number of clusters is necessary and not even the number of clusters or the number of objects nee d to be known. With this approach high quality segmentation results are achieved. (More)

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Paper citation in several formats:
Kisner, H. and Thomas, U. (2018). Segmentation of 3D Point Clouds using a New Spectral Clustering Algorithm Without a-priori Knowledge. In Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP; ISBN 978-989-758-290-5; ISSN 2184-4321, SciTePress, pages 315-322. DOI: 10.5220/0006549303150322

@conference{visapp18,
author={Hannes Kisner. and Ulrike Thomas.},
title={Segmentation of 3D Point Clouds using a New Spectral Clustering Algorithm Without a-priori Knowledge},
booktitle={Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP},
year={2018},
pages={315-322},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006549303150322},
isbn={978-989-758-290-5},
issn={2184-4321},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2018) - Volume 4: VISAPP
TI - Segmentation of 3D Point Clouds using a New Spectral Clustering Algorithm Without a-priori Knowledge
SN - 978-989-758-290-5
IS - 2184-4321
AU - Kisner, H.
AU - Thomas, U.
PY - 2018
SP - 315
EP - 322
DO - 10.5220/0006549303150322
PB - SciTePress