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JCR Impact Factor: 0.699
JCR 5-Year IF: 0.674
Issues per year: 4
Current issue: Aug 2018
Next issue: Nov 2018
Avg review time: 83 days


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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

2018-Jun-27
Clarivate Analytics published the InCites Journal Citations Report for 2017. The JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.699, and the JCR 5-Year Impact Factor is 0.674.

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-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.

Read More »


    
 

  2/2017 - 9

Wavelet Energy and the Usefulness of its Powers in Motion Detection

VUJOVIC, I. See more information about VUJOVIC, I. on SCOPUS See more information about VUJOVIC, I. on IEEExplore See more information about VUJOVIC, I. on Web of Science, KUZMANIC, I. See more information about KUZMANIC, I. on SCOPUS See more information about KUZMANIC, I. on SCOPUS See more information about KUZMANIC, I. on Web of Science
 
Click to see author's profile in 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 (1,686 KB) | Citation | Downloads: 227 | Views: 642

Author keywords
discrete wavelet transform, image motion analysis, morphological operations, motion detection, wavelet coefficients

References keywords
detection(12), wavelet(11), energy(10), image(9), signal(5), moving(5), science(4), processing(4), motion(4), backg(4)
Blue keywords are present in both the references section and the paper title.

About this article
Date of Publication: 2017-05-31
Volume 17, Issue 2, Year 2017, On page(s): 61 - 70
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2017.02009
Web of Science Accession Number: 000405378100009
SCOPUS ID: 85020062900

Abstract
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The potential for the usage of energy exponents in motion detection from video sequences is explored. The wavelet domain was chosen for the research due to the optimality of Hilbert's space for energy calculations and Parseval's equation for energy equivalence between domains. Five algorithms were considered: wavelet energy motion detection algorithm based on wavelet pairs and buffer, listed in the references, and four which are the contributions of this paper: modification by the application of different wavelet pairs, a modified algorithm without buffer, a modified algorithm without buffer and pairs, newly developed algorithms for energy exponents with and without buffer, but with wavelet pairs. The considered algorithms are background subtraction algorithms modified not to use pixels values, but rather energy/energy exponent backgrounds and the current situation models. These models are described by wavelet descriptors, the introduction of which is the contribution of this paper. They are compared by standard statistical criteria and execution time. The results suggest that an increase in the energy exponent decreases precision, recall and F-measure. However, the percentage of correct classifications remains almost constant. Higher exponentials reduce noise, but are more susceptible to shadows, the waving tree effect and similar abnormalities. Algorithms without buffers are less robust to illumination changes.


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

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[2] M. Seiferta, H. S. Hock, "The Independent Detection of Motion Energy and Counterchange: Flexibility in Motion Detection," Vision Research, vol. 98, pp. 61–71, 2014.
[CrossRef] [SCOPUS Times Cited 4]


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[CrossRef] [SCOPUS Times Cited 9]


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[CrossRef]


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[CrossRef] [SCOPUS Times Cited 11]


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[14] S. Y. Elhabian, K. M. E. Sayed, S. H. Ahmed, "Moving Object Detection in Spatial Domain Using Background Removal Techniques - State-of-Art," Recent Patents on Computer Science, vol. 1, no. 1, pp. 32-54, 2008.
[CrossRef]


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[CrossRef]


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[CrossRef]


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References Weight

Web of Science® Citations for all references: 317 TCR
SCOPUS® Citations for all references: 714 TCR

Web of Science® Average Citations per reference: 10 ACR
SCOPUS® Average Citations per reference: 23 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 2018-10-23 11:25 in 176 seconds.




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Faculty of Electrical Engineering and Computer Science
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