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Authors: Hasan Almassri 1 ; Tim Dackermann 2 and Norbert Haala 3

Affiliations: 1 Institute for Photogrammetry, University of Stuttgart, Germany, Robert Bosch GmbH Company, Reutlingen and Germany ; 2 Robert Bosch GmbH Company, Reutlingen and Germany ; 3 Institute for Photogrammetry, University of Stuttgart and Germany

ISBN: 978-989-758-351-3

Keyword(s): Clustering, Real Time, Superpixel, Segmentation.

Abstract: mDBSCAN is an improved version of DBSCAN (Density Based Spatial Clustering of Applications with Noise) superpixel segmentation. Unlike DBSCAN algorithm, the proposed algorithm has an automatic threshold based on the colour and gradient information. The proposed algorithm performs under different colour space such as RGB, Lab and grey images using a novel distance measurement. The experimental results demonstrate that the proposed algorithm outperforms the state of the art algorithms in terms of boundary adherence and segmentation accuracy with low computational cost (30 frames/s).

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Paper citation in several formats:
Almassri, H.; Dackermann, T. and Haala, N. (2019). mDBSCAN: Real Time Superpixel Segmentation by DBSCAN Clustering based on Boundary Term.In Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-351-3, pages 283-291. DOI: 10.5220/0007249302830291

@conference{icpram19,
author={Hasan Almassri. and Tim Dackermann. and Norbert Haala.},
title={mDBSCAN: Real Time Superpixel Segmentation by DBSCAN Clustering based on Boundary Term},
booktitle={Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2019},
pages={283-291},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007249302830291},
isbn={978-989-758-351-3},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - mDBSCAN: Real Time Superpixel Segmentation by DBSCAN Clustering based on Boundary Term
SN - 978-989-758-351-3
AU - Almassri, H.
AU - Dackermann, T.
AU - Haala, N.
PY - 2019
SP - 283
EP - 291
DO - 10.5220/0007249302830291

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