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Depth-Image Segmentation Based on Evolving Principles for 3D Sensing of Structured Indoor Environments
This paper presents an approach of depth image segmentation based on the Evolving Principal Component Clustering (EPCC) method, which exploits data locality in an ordered data stream. The parameters of linear prototypes, which are used to describe different clusters, are estimated in a recursive man...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271552/ https://www.ncbi.nlm.nih.gov/pubmed/34198980 http://dx.doi.org/10.3390/s21134395 |
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author | Antić, Miloš Zdešar, Andrej Škrjanc, Igor |
author_facet | Antić, Miloš Zdešar, Andrej Škrjanc, Igor |
author_sort | Antić, Miloš |
collection | PubMed |
description | This paper presents an approach of depth image segmentation based on the Evolving Principal Component Clustering (EPCC) method, which exploits data locality in an ordered data stream. The parameters of linear prototypes, which are used to describe different clusters, are estimated in a recursive manner. The main contribution of this work is the extension and application of the EPCC to 3D space for recursive and real-time detection of flat connected surfaces based on linear segments, which are all detected in an evolving way. To obtain optimal results when processing homogeneous surfaces, we introduced two-step filtering for outlier detection within a clustering framework and considered the noise model, which allowed for the compensation of characteristic uncertainties that are introduced into the measurements of depth sensors. The developed algorithm was compared with well-known methods for point cloud segmentation. The proposed approach achieves better segmentation results over longer distances for which the signal-to-noise ratio is low, without prior filtering of the data. On the given database, an average rate higher than 90% was obtained for successfully detected flat surfaces, which indicates high performance when processing huge point clouds in a non-iterative manner. |
format | Online Article Text |
id | pubmed-8271552 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82715522021-07-11 Depth-Image Segmentation Based on Evolving Principles for 3D Sensing of Structured Indoor Environments Antić, Miloš Zdešar, Andrej Škrjanc, Igor Sensors (Basel) Article This paper presents an approach of depth image segmentation based on the Evolving Principal Component Clustering (EPCC) method, which exploits data locality in an ordered data stream. The parameters of linear prototypes, which are used to describe different clusters, are estimated in a recursive manner. The main contribution of this work is the extension and application of the EPCC to 3D space for recursive and real-time detection of flat connected surfaces based on linear segments, which are all detected in an evolving way. To obtain optimal results when processing homogeneous surfaces, we introduced two-step filtering for outlier detection within a clustering framework and considered the noise model, which allowed for the compensation of characteristic uncertainties that are introduced into the measurements of depth sensors. The developed algorithm was compared with well-known methods for point cloud segmentation. The proposed approach achieves better segmentation results over longer distances for which the signal-to-noise ratio is low, without prior filtering of the data. On the given database, an average rate higher than 90% was obtained for successfully detected flat surfaces, which indicates high performance when processing huge point clouds in a non-iterative manner. MDPI 2021-06-27 /pmc/articles/PMC8271552/ /pubmed/34198980 http://dx.doi.org/10.3390/s21134395 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Antić, Miloš Zdešar, Andrej Škrjanc, Igor Depth-Image Segmentation Based on Evolving Principles for 3D Sensing of Structured Indoor Environments |
title | Depth-Image Segmentation Based on Evolving Principles for 3D Sensing of Structured Indoor Environments |
title_full | Depth-Image Segmentation Based on Evolving Principles for 3D Sensing of Structured Indoor Environments |
title_fullStr | Depth-Image Segmentation Based on Evolving Principles for 3D Sensing of Structured Indoor Environments |
title_full_unstemmed | Depth-Image Segmentation Based on Evolving Principles for 3D Sensing of Structured Indoor Environments |
title_short | Depth-Image Segmentation Based on Evolving Principles for 3D Sensing of Structured Indoor Environments |
title_sort | depth-image segmentation based on evolving principles for 3d sensing of structured indoor environments |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271552/ https://www.ncbi.nlm.nih.gov/pubmed/34198980 http://dx.doi.org/10.3390/s21134395 |
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