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Stefan cel Mare
University of Suceava
Faculty of Electrical Engineering and
Computer Science
13, Universitatii Street
Suceava - 720229

Print ISSN: 1582-7445
Online ISSN: 1844-7600
WorldCat: 643243560
doi: 10.4316/AECE


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  3/2012 - 11

Object Extraction from Architecture Scenes through 3D Local Scanned Data Analysis

NING, X. See more information about NING, X. on SCOPUS See more information about NING, X. on IEEExplore See more information about NING, X. on Web of Science, WANG, Y. See more information about WANG, Y. on SCOPUS See more information about WANG, Y. on SCOPUS See more information about WANG, Y. on Web of Science
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Download PDF pdficon (858 KB) | Citation | Downloads: 603 | Views: 3,419

Author keywords
terrestrial laser scanner, point cloud segmentation, similarity measurement, nearest neighboring graph

References keywords
segmentation(8), point(7), image(7), range(5), pattern(5), clouds(5), vision(4), transform(4), robust(4)
No common words between the references section and the paper title.

About this article
Date of Publication: 2012-08-31
Volume 12, Issue 3, Year 2012, On page(s): 73 - 78
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2012.03011
Web of Science Accession Number: 000308290500011
SCOPUS ID: 84865851513

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Terrestrial laser scanning becomes a standard way for acquiring 3D data of complex outdoor objects. The processing of huge number of points and recognition of different objects inside become a new challenge, especially in the case where objects are included. In this paper, a new approach is proposed to classify objects through an analysis on shape information of the point cloud data. The scanned scene is constructed using k Nearest Neighboring (k-NN), and then similarity measurement between points is defined to cluster points with similar primitive shapes. Moreover, we introduce a combined geometrical criterion to refine the over-segmented results. To achieve more detail information, a residual based segmentation is adopted to refine the segmentation of architectural objects into more parts with different shape properties. Experimental results demonstrate that this approach can be used as a robust way to extract different objects in the scenes.

References | Cited By  «-- Click to see who has cited this paper

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[7] Ruwen Schnabel, Roland Wahl, and Reinhard Klein. "Shape detection in point clouds," Technical Report CG-2006-2, Universitat Bonn, January 2006.

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[12] Kourosh Khoshelham, "Extending generalized hough transform to detect 3d objects in laser range data," Transform, 2007, XXXV, pp. 206-210.

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[17] Jie Chen and Baoquan Chen. "Architectural modeling from sparsely scanned range data," Int. J. Comput. Vision, 2008, 78(2-3), pp.223-236.
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[18] Aleksey Golovinskiy and Thomas Funkhouser, "Min-cut based segmentation of point clouds," In IEEE Workshop on Search in 3D and Video (S3DV) at ICCV, 2009.
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[22] Xiaojuan Ning, Xiaopeng Zhang, Yinghui Wang, Tree segmentation from scanned scene data. In PMA, December 2009.
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References Weight

Web of Science® Citations for all references: 21,550 TCR
SCOPUS® Citations for all references: 27,984 TCR

Web of Science® Average Citations per reference: 937 ACR
SCOPUS® Average Citations per reference: 1,217 ACR

TCR = Total Citations for References / ACR = Average Citations per Reference

We introduced in 2010 - for the first time in scientific publishing, the term "References Weight", as a quantitative indication of the quality ... Read more

Citations for references updated on 2022-05-20 03:21 in 127 seconds.

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Faculty of Electrical Engineering and Computer Science
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