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JCR Impact Factor: 0.650
JCR 5-Year IF: 0.639
Issues per year: 4
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Next issue: Feb 2020
Avg review time: 71 days


PUBLISHER

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

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


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2019-Dec-16
Starting on the 15th of December 2019 all paper authors are required to enter their SCOPUS IDs. You may use the free SCOPUS ID lookup form to find yours in case you don't remember it.

2019-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2018. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.650, and the JCR 5-Year Impact Factor is 0.639.

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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: 428 | Views: 2,851

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

Abstract
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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

[1] Roth, G., Levine, M. D., "Extracting geometric primitives," CVGIP: Image Underst. 58(1), pp.1-22 (1993).
[CrossRef] [SCOPUS Times Cited 32]


[2] Phil Torr and Andrew Zisserman, "Robust computation and parameterization of multiple view relations," In ICCV'98: Proceedings of the Sixth International Conference on Computer Vision, Washington, DC, USA, 1998, pp.727.
[CrossRef] [Web of Science Times Cited 49]


[3] Torr, P. H.S ., Zisserman, A., "Mlesac: a new robust estimator with application to estimating image geometry," Comput. Vis. Image Underst. 78(1), pp. 138-156, 2000.
[CrossRef] [Web of Science Times Cited 929] [SCOPUS Times Cited 1247]


[4] Liang Xinhe, Liang Jin, Xiao Zhenzhong, Liu Jianwei, and Guo Cheng, "Study on Multi-Views Point Clouds Registration," Adv. Sci. Lett, 2011, 4, pp.2885-2889.
[CrossRef] [Web of Science Times Cited 1] [SCOPUS Times Cited 2]


[5] Fischler, M. A., Bolles, R. C., "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography," Commun. ACM. 1981, 24, pp. 381-395
[CrossRef] [Web of Science Times Cited 10049] [SCOPUS Times Cited 13407]


[6] Nister, D. "Preemptive ransac for live structure and motion estimation," Mach. Vision Appl. 16(5), pp. 321-329, 2005.
[CrossRef] [Web of Science Times Cited 155] [SCOPUS Times Cited 191]


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

[8] Liangliang Nan, Andrei Sharf, Hao Zhang, DanielCohen-Or, and Baoquan Chen. "Smartboxes for interactive urban reconstruction," ACM Trans. Graph., 2010, 29, pp 93:1-93:10.
[CrossRef] [Web of Science Times Cited 75] [SCOPUS Times Cited 98]


[9] Ballard, D. H. Generalizing the hough transform to detect arbitrary shapes. Pattern recognition. 1981, 13(2), pp. 111-122
[CrossRef] [Web of Science Times Cited 2183] [SCOPUS Times Cited 2908]


[10] George Vosselman, Er Dijkman. "3d building model reconstruction from point clouds and ground plans," International Archives of Photogrammetry and Remote Sensing, 2001, Volume XXXIV- 3/W4 (22-24), pp. 37-43.

[11] T. Rabbani and F. Van Den Heuvel. "Efficient hough transform for automatic detection of cylinders in point clouds," In ISPRS WG III/3, III/4, V/3 workshop. 2005, pp.60-65.

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

[13] P. J. Besl and R. C. Jain, "Segmentation through variable-order surface fitting," IEEE Transaction on Pattern Analysis and Machine Intelligence, 1988, 10(2), pp. 167-92.
[CrossRef] [Web of Science Times Cited 500] [SCOPUS Times Cited 728]


[14] I. S. Chang and R. H. Park, "Segmentation based on fusion of range and intensity images using robust trimmed methods," Pattern Reconition, 2001, 34, pp. 1951-1962.
[CrossRef] [Web of Science Times Cited 16] [SCOPUS Times Cited 23]


[15] Klaus Koster and Michael Spann, "MIR: An approach to robust clustering application to range image segmentation," IEEE Trans. Pattern Anal. Mach. Intell., 2000, 22(5), pp. 430-444.
[CrossRef] [Web of Science Times Cited 44] [SCOPUS Times Cited 65]


[16] Guoyu Wang, Zweitze Houkes, Guangrong Ji, Bing Zheng, and Xin Li, An estimation-based approach for range image segmentation: On the reliability of primitive extraction. Pattern Recognition, 2003, 36(1), pp.157-169,
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 14]


[17] Jie Chen and Baoquan Chen. "Architectural modeling from sparsely scanned range data," Int. J. Comput. Vision, 2008, 78(2-3), pp.223-236.
[CrossRef] [Web of Science Times Cited 43] [SCOPUS Times Cited 58]


[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.
[CrossRef] [SCOPUS Times Cited 151]


[19] Pedro F. Felzenszwalb and Daniel P. Huttenlocher, "Efficient graph based image segmentation," International Journal of Computer Vision, 2004, 59(2), pp. 167-92.
[CrossRef] [Web of Science Times Cited 2791] [SCOPUS Times Cited 3961]


[20] ZhangLin. Cheng,XiaoPeng Zhang, "Estimating differential quantities from point cloud based on a linear fitting of normal vectors," Science in China Series F: Information Sciences, 2009, 52, pp. 431-444.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 12]


[21] Xiaojuan Ning, Xiaopeng Zhang, Yinghui Wang, Segmentation of architecture shape information from 3d point cloud. In The 8th ACM SIGGRAPH International Conference on VRCAI, 2009.12.
[CrossRef] [SCOPUS Times Cited 5]


[22] Xiaojuan Ning, Xiaopeng Zhang, Yinghui Wang, Tree segmentation from scanned scene data. In PMA, December 2009.
[CrossRef] [SCOPUS Times Cited 3]




References Weight

Web of Science® Citations for all references: 16,854 TCR
SCOPUS® Citations for all references: 22,905 TCR

Web of Science® Average Citations per reference: 733 ACR
SCOPUS® Average Citations per reference: 996 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 2020-01-17 09:08 in 126 seconds.




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Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


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