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JCR Impact Factor: 0.650
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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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LATEST NEWS

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.

2018-May-31
Starting today, the minimum number a pages for a paper is 8, so all submitted papers should have 8, 10 or 12 pages. No exceptions will be accepted.

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  3/2019 - 2

Top-Down Approach to the Automatic Extraction of Individual Trees from Scanned Scene Point Cloud Data

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, TIAN, G. See more information about  TIAN, G. on SCOPUS See more information about  TIAN, G. on SCOPUS See more information about TIAN, G. 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 (1,044 KB) | Citation | Downloads: 117 | Views: 144

Author keywords
computer graphics, computer aided analysis, feature extraction, object segmentation, pattern recognition

References keywords
laser(19), sensing(18), remote(18), scanning(13), mobile(13), data(13), trees(11), tree(11), point(11), photogrammetry(10)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2019-08-31
Volume 19, Issue 3, Year 2019, On page(s): 11 - 18
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2019.03002
Web of Science Accession Number: 000486574100002
SCOPUS ID: 85072171970

Abstract
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Urban trees are essential elements in outdoor scenes recorded via terrestrial laser scanning. Although considerable interest has been centered on tree detection and reconstruction in recent years, trees cannot be easily extracted from dense and unorganized data because of the complexity and diversity of trees. In this paper, we present a top-down approach for detecting trees from point cloud data acquired for dense urban areas. Appropriate feature subsets are chosen, and then the candidate tree clusters are selected via a binary classification. After distinguishing the 3D points belonging to tree-like objects, individual trees are extracted by spectral clustering. Furthermore, a weighted constraint rule is proposed to refine the individual tree clusters. The methodology is tested on five real-world datasets that include different varieties of trees. The results reveal that most of the individual trees can be correctly detected and extracted. The results are quantitatively evaluated and reveal a global F1 value of approximately 97 percent and a precision of approximately 98 percent. Comparative analysis on the datasets is also provided to prove the effectiveness of our proposed method.


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

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References Weight

Web of Science® Citations for all references: 2,212 TCR
SCOPUS® Citations for all references: 0

Web of Science® Average Citations per reference: 63 ACR
SCOPUS® Average Citations per reference: 0

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 2019-10-14 12:31 in 218 seconds.




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


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