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JCR Impact Factor: 0.595
JCR 5-Year IF: 0.661
Issues per year: 4
Current issue: Aug 2017
Next issue: Nov 2017
Avg review time: 77 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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Wind Speed Prediction with Wavelet Time Series Based on Lorenz Disturbance, ZHANG, Y., WANG, P., CHENG, P., LEI, S.
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LATEST NEWS

2017-Jun-14
Thomson Reuters published the Journal Citations Report for 2016. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.595, and the JCR 5-Year Impact Factor is 0.661.

2017-Apr-04
We have the confirmation Advances in Electrical and Computer Engineering will be included in the EBSCO database.

2017-Feb-16
With new technologies, such as mobile communications, internet of things, and wide applications of social media, organizations generate a huge volume of data, much faster than several years ago. Big data, characterized by high volume, diversity and velocity, increasingly drives decision making and is changing the landscape of business intelligence, from governments to private organizations, from communities to individuals. Big data analytics that discover insights from evidences has a high demand for computing efficiency, knowledge discovery, problem solving, and event prediction. We dedicate a special section of Issue 4/2017 to Big Data. Prospective authors are asked to make the submissions for this section no later than the 31st of May 2017, placing "BigData - " before the paper title in OpenConf.

2017-Jan-30
We have the confirmation Advances in Electrical and Computer Engineering will be included in the Gale database.

2016-Dec-17
IoT is a new emerging technology domain which will be used to connect all objects through the Internet for remote sensing and control. IoT uses a combination of WSN (Wireless Sensor Network), M2M (Machine to Machine), robotics, wireless networking, Internet technologies, and Smart Devices. We dedicate a special section of Issue 2/2017 to IoT. Prospective authors are asked to make the submissions for this section no later than the 31st of March 2017, placing "IoT - " before the paper title in OpenConf.

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  4/2011 - 18

New Method to Detect Salient Objects in Image Segmentation using Hypergraph Structure

GANEA, E. See more information about GANEA, E. on SCOPUS See more information about GANEA, E. on IEEExplore See more information about GANEA, E. on Web of Science, BURDESCU, D. D. See more information about  BURDESCU, D. D. on SCOPUS See more information about  BURDESCU, D. D. on SCOPUS See more information about BURDESCU, D. D. on Web of Science, BREZOVAN, M. See more information about BREZOVAN, M. on SCOPUS See more information about BREZOVAN, M. on SCOPUS See more information about BREZOVAN, M. on Web of Science
 
Click to see author's profile on See more information about the author on SCOPUS SCOPUS, See more information about the author on IEEE Xplore IEEE Xplore, See more information about the author on Web of Science Web of Science

Download PDF pdficon (451 KB) | Citation | Downloads: 943 | Views: 2,504

Author keywords
feature extraction, image processing, image segmentation, hypergraph data structures, object detection

References keywords
segmentation(12), image(12), pattern(10), vision(8), recognition(6), graph(6), multimedia(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2011-11-30
Volume 11, Issue 4, Year 2011, On page(s): 111 - 116
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2011.04018
Web of Science Accession Number: 000297764500018
SCOPUS ID: 84856623803

Abstract
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This paper presents a method for detection of salient objects from images. The proposed algorithms for image segmentation and objects detection use a hexagonal representation of the image pixels and a hypergraph structure to process this hierarchal structure. The main goal of the method is to obtain salient regions, which may be associated with semantic labels. The designed algorithms use color characteristic and syntactic features for image segmentation. The object-oriented model used for storing the results of the segmentation and detection allows directly annotation of regions without a processing of these. The experiments showed that the presented method is robust and accurate comparing with others public methods used for salient objects detection.


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

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[CrossRef] [Web of Science Times Cited 115] [SCOPUS Times Cited 145]


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[CrossRef]


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[7] L. Guigues, L. M. Herve and L.-P. Cocquerez. "The hierarchy of the cocoons of a graph and its application to image segmentation", In Pattern Recognition Letters, 24, pp. 1059 - 1066, 2003.
[CrossRef] [Web of Science Times Cited 30] [SCOPUS Times Cited 46]


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[CrossRef] [Web of Science Times Cited 96] [SCOPUS Times Cited 110]


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[CrossRef]


[12] A. Bretto and L. Gillibert. "Hypergraph-based imge representation", In Graph-Based Representations in Pattern Recognition, pp. 1-11, 2005.

[13] E. Ganea and M. Brezovan. "An Hypegraph Object-Oriented Model for Image Segmentation and Annotation", In Proceedings of the International Multiconference on Computer Science and Information Technology, pp. 695 - 701, 2010.

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[CrossRef] [Web of Science Times Cited 848] [SCOPUS Times Cited 1319]


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[17] I. Jonyer, L. Holder and D. Cook. "Concept Formation Using Graph Grammars", In Proceedings of the KDD Workshop on Multi-Relational Data Mining, 2002.

[18] L. B. Holder. "Empirical Substructure Discovery", In Proceedings of the Sixth International Workshop on Machine Learning, pp. 133-136, 1989.

[19] D. Martin, C. Fowlkes, D. Tal and J. Malik. "A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics", In IEEE International Conference on Computer Vision, pp. 416 - 423, 2001.
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[20] M. Donoser and H. Bischof. "ROI-SEG: Unsupervised Color Segmentation by Combining Differently Focused Sub Results", In IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007.
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[CrossRef]


[22] V. Movahedi and J. H. Elder. "Segmenting Salient Objects: How do we measure success?", Poster at CVR09, Centre for Vision Research CVR Conference, 2009.

[23] A. Y Yang., J. Wright, M. Yi and S. S. Sastry. "Unsupervised Segmentation of Natural Images via Lossy Data Compression", In Computer Vision and Image Understanding, vol. 110, pp. 212 - 225, 2008.
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References Weight

Web of Science® Citations for all references: 8,218 TCR
SCOPUS® Citations for all references: 14,654 TCR

Web of Science® Average Citations per reference: 342 ACR
SCOPUS® Average Citations per reference: 611 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 2017-09-22 10:41 in 87 seconds.




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


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