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Mathematical Morphology on Irregularly Sampled Data Applied to Segmentation of 3D Point Clouds of Urban Scenes
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction.ORCID iD: 0000-0002-0612-558x
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MINES ParisTech, PSL Research University, CMM - Centre for Mathematical Morphology.
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Computerized Image Analysis and Human-Computer Interaction. Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Visual Information and Interaction. Uppsala University, Disciplinary Domain of Medicine and Pharmacy, Faculty of Medicine, Department of Surgical Sciences, Radiology.ORCID iD: 0000-0001-7764-1787
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2019 (English)In: International Symposium on Mathematical Morphology and Its Applications to Signal and Image Processing, 2019Conference paper, Published paper (Refereed)
Abstract [en]

This paper proposes an extension of mathematical morphology on irregularly sampled signals to 3D point clouds. The proposed method is applied to the segmentation of urban scenes to show its applicability to the analysis of point cloud data. Applying the proposed operators has the desirable side-effect of homogenizing signals that are sampled heterogeneously. In experiments we show that the proposed segmentation algorithm yields good results on the Paris-rue-Madame database and is robust in terms of sampling density, i.e. yielding similar labelings for more sparse samplings of the same scene.

Place, publisher, year, edition, pages
2019.
National Category
Signal Processing
Research subject
Computerized Image Processing
Identifiers
URN: urn:nbn:se:uu:diva-388524DOI: 10.1007/978-3-030-20867-7_29ISBN: 978-3-030-20866-0 (print)ISBN: 978-3-030-20867-7 (electronic)OAI: oai:DiVA.org:uu-388524DiVA, id: diva2:1333651
Conference
International Symposium on Mathematical Morphology (ISMM 2019)
Funder
Swedish Research Council, 2014-5983Available from: 2019-07-01 Created: 2019-07-01 Last updated: 2019-10-17
In thesis
1. Precise Image-Based Measurements through Irregular Sampling
Open this publication in new window or tab >>Precise Image-Based Measurements through Irregular Sampling
2019 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Noggranna bildbaserade mätningar via irreguljär sampling
Abstract [en]

Mathematical morphology is a theory that is applicable broadly in signal processing, but in this thesis we focus mainly on image data. Fundamental concepts of morphology include the structuring element and the four operators: dilation, erosion, closing, and opening. One way of thinking about the role of the structuring element is as a probe, which traverses the signal (e.g. the image) systematically and inspects how well it "fits" in a certain sense that depends on the operator.

Although morphology is defined in the discrete as well as in the continuous domain, often only the discrete case is considered in practice. However, commonly digital images are a representation of continuous reality and thus it is of interest to maintain a correspondence between mathematical morphology operating in the discrete and in the continuous domain. Therefore, much of this thesis investigates how to better approximate continuous morphology in the discrete domain. We present a number of issues relating to this goal when applying morphology in the regular, discrete case, and show that allowing for irregularly sampled signals can improve this approximation, since moving to irregularly sampled signals frees us from constraints (namely those imposed by the sampling lattice) that harm the correspondence in the regular case. The thesis develops a framework for applying morphology in the irregular case, using a wide range of structuring elements, including non-flat structuring elements (or structuring functions) and adaptive morphology. This proposed framework is then shown to better approximate continuous morphology than its regular, discrete counterpart.

Additionally, the thesis contains work dealing with regularly sampled images using regular, discrete morphology and weighting to improve results. However, these cases can be interpreted as specific instances of irregularly sampled signals, thus naturally connecting them to the overarching theme of irregular sampling, precise measurements, and mathematical morphology.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2019. p. 63
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1869
Keywords
image analysis, image processing, mathematical morphology, irregular sampling, adaptive morphology, missing samples, continuous morphology, path opening.
National Category
Signal Processing Other Computer and Information Science
Research subject
Computerized Image Processing
Identifiers
urn:nbn:se:uu:diva-395205 (URN)978-91-513-0783-1 (ISBN)
Public defence
2019-12-06, Room 2446, ITC, Lägerhyddsvägen 2, Uppsala, 13:00 (English)
Opponent
Supervisors
Funder
Swedish Research Council, 2014-5983
Available from: 2019-11-13 Created: 2019-10-17 Last updated: 2019-11-13

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Asplund, TeoStrand, Robin

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