Click to open the HelpDesk interface
AECE - Front page banner

Menu:


FACTS & FIGURES

JCR Impact Factor: 0.700
JCR 5-Year IF: 0.700
SCOPUS CiteScore: 1.8
Issues per year: 4
Current issue: Nov 2024
Next issue: Feb 2025
Avg review time: 56 days
Avg accept to publ: 60 days
APC: 300 EUR


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


TRAFFIC STATS

3,030,196 unique visits
1,178,336 downloads
Since November 1, 2009



Robots online now
Googlebot
bingbot


SCOPUS CiteScore

SCOPUS CiteScore


SJR SCImago RANK

SCImago Journal & Country Rank




TEXT LINKS

Anycast DNS Hosting
MOST RECENT ISSUES

 Volume 24 (2024)
 
     »   Issue 4 / 2024
 
     »   Issue 3 / 2024
 
     »   Issue 2 / 2024
 
     »   Issue 1 / 2024
 
 
 Volume 23 (2023)
 
     »   Issue 4 / 2023
 
     »   Issue 3 / 2023
 
     »   Issue 2 / 2023
 
     »   Issue 1 / 2023
 
 
 Volume 22 (2022)
 
     »   Issue 4 / 2022
 
     »   Issue 3 / 2022
 
     »   Issue 2 / 2022
 
     »   Issue 1 / 2022
 
 
 Volume 21 (2021)
 
     »   Issue 4 / 2021
 
     »   Issue 3 / 2021
 
     »   Issue 2 / 2021
 
     »   Issue 1 / 2021
 
 
  View all issues  


FEATURED ARTICLE

A Proposed Signal Reconstruction Algorithm over Bandlimited Channels for Wireless Communications, ASHOUR, A., KHALAF, A., HUSSEIN, A., HAMED, H., RAMADAN, A.
Issue 1/2023

AbstractPlus






LATEST NEWS

2024-Jun-20
Clarivate Analytics published the InCites Journal Citations Report for 2023. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.700 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.600.

2023-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2022. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.800 (0.700 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 1.000.

2023-Jun-05
SCOPUS published the CiteScore for 2022, computed by using an improved methodology, counting the citations received in 2019-2022 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2022 is 2.0. For "General Computer Science" we rank #134/233 and for "Electrical and Electronic Engineering" we rank #478/738.

2022-Jun-28
Clarivate Analytics published the InCites Journal Citations Report for 2021. The InCites JCR Impact Factor of Advances in Electrical and Computer Engineering is 0.825 (0.722 without Journal self-cites), and the InCites JCR 5-Year Impact Factor is 0.752.

2022-Jun-16
SCOPUS published the CiteScore for 2021, computed by using an improved methodology, counting the citations received in 2018-2021 and dividing the sum by the number of papers published in the same time frame. The CiteScore of Advances in Electrical and Computer Engineering for 2021 is 2.5, the same as for 2020 but better than all our previous results.

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
 
Extra paper information in View the paper record and citations in Google Scholar View the paper record and similar papers in Microsoft Bing View the paper record and similar papers in Semantic Scholar the AI-powered research tool
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: 890 | Views: 2,845

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
Quick view
Full text preview
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

[1] Q. Xie, Q. Long, S. Mita, C. Guo, A. Jiang, "Image Fusion Based on Multi-objective Optimization," International Journal of Wavelets, Multiresolution and Information, vol. 12, no. 2, pp. 1450017, 2014.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 8]


[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] [Web of Science Times Cited 3] [SCOPUS Times Cited 4]


[3] G. T. Zhai, X. L. Wu, X. K. Yang, W. S. Lin, W. J. Zhang, "A Psychovisual Quality Metric in Free - Energy Principle," IEEE Transactions on Image Processing, vol. 21, no. 1, pp. 41-52, 2012.
[CrossRef] [Web of Science Times Cited 221] [SCOPUS Times Cited 247]


[4] D. Y. Huang, T. W. Lin, W. C. Hu, C. H. Cheng, "Gait Recognition Based on Gabor Wavelets and Modified Gait Energy Image for Human Identification," Journal of Electronic Imaging, vol. 22, no. 4, pp. 043039, 2013.
[CrossRef] [Web of Science Times Cited 9] [SCOPUS Times Cited 18]


[5] D. J. Joo, "Damage detection and system identification using a wavelet energy based approach," Columbia University, PhD thesis, 2012.

[6] Q. He, "Vibration Signal Classification by Wavelet Packet Energy Flow Manifold Learning," Journal of Sound and Vibration, vol. 332, no. 7, pp. 1881-1894, 2012.
[CrossRef] [Web of Science Times Cited 101] [SCOPUS Times Cited 117]


[7] Y. Yang, S. Huang, J. Gao, Z. Qian, "Multi-focus Image Fusion Using an Effective Discrete Wavelet Transform Based Algorithm," Measurement Science and Review, vol. 14, no. 2, pp. 102-108, 2014.
[CrossRef] [Web of Science Times Cited 41] [SCOPUS Times Cited 60]


[8] K. Y. E. Wong, G. Sainarayanan, A. Chekima, "Palmprint Identification Using Wavelet Energy," in Proc. International Conference on Intelligent and Advanced Systems, Kuala Lumpur, Malaysia, 2007, pp. 714-719,
[CrossRef] [SCOPUS Times Cited 15]


[9] J. A. Dobrosotskaya, A. L. Bertozzi, "Analysis of the Wavelet Ginzburg-Landau Energy in Image Applications with Edges," SIAM Journal on Imaging Sciences, vol. 6, no. 1, pp. 698–729, 2013.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 11]


[10] M. Vosvrda, J. Schürrer, "Wavelet Coefficients Energy Redistribution and Heisenberg Principle of Uncertainty," in Proc. Mathematical Methods in Economics, Cheb, Czech Republic, 2015, pp. 894-899.

[11] P. Sebastian, A. Pradeep, "A comparative Study of Artificial Neural Network Based Power Quality Signal Classification Systems with Wavelet Coefficients and Wavelet Based Energy Distribution," International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering, vol. 5, no. 4, pp. 2929-2934, 2016.
[CrossRef]


[12] K. Qian, C. Janott, Z. Zhang, C. Heiser, B. Schuller, "Wavelet Features for Classification of Vote Snore Sounds," in Proc. IEEE International Conference on Acoustics, Speech and Signal Processing, Shanghai, China, 2016, pp. 221-225.
[CrossRef] [SCOPUS Times Cited 31]


[13] P. K. Bhatia, A. Sharma, "Epilepsy Seizure Detection Using Wavelet Support Vector Machine Classifier," International Journal of Bio-Science and Bio-Technology, vol. 8, no. 2, pp. 11-22, 2016.
[CrossRef] [SCOPUS Times Cited 9]


[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]


[15] S. Manchanda, S. Sharma, "Analysis of computer vision based techniques for motion detection," in Proc. 6th Int. Conf. on Cloud System and Big Data Engineering, Uttar Pradesh, Noida, India, 2016, pp. 445-450.
[CrossRef] [SCOPUS Times Cited 19]


[16] S. Kumar, J. S. Yadav, "Segmentation of moving objects using background subtraction method in complex environments," Radioengineering, vol. 25, pp. 399-408, Jun. 2016.
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 12]


[17] X. Hu, J. Zheng, "An improved moving object detection algorithm based on Gaussian mixture models," Open Journal of Applied Sciences, vol. 6, pp. 449-456, Jul. 2016.
[CrossRef]


[18] M. Shakeri, H. Zhang, "COROLA: A sequential solution to moving object detection using low-rank approximation," Computer Vision and Image Understanding, vol. 146, pp. 27-39, May 2016.
[CrossRef] [Web of Science Times Cited 54] [SCOPUS Times Cited 61]


[19] F. Trèves, "Topological Vector Spaces, Distributions and Kernels," pp. 95-126, Academic Press, 1995.

[20] B. Hassibi, A. H. Sayed, T. Kailath, "Indefinite-Quadratic Estimation and Control: A Unified Approach to H2 and H8 Theories," pp. 81-107, Society for Industrial and Applied Mathematics, 1999.
[CrossRef]


[21] N. A. Bruisma, M. A. Steinbuch, "Fast Algorithm to Compute the H8-Norm of a Transfer Function Matrix," System & Control Letters, vol. 14, no. 4, pp. 287-293, 1990.
[CrossRef] [Web of Science Times Cited 190] [SCOPUS Times Cited 221]


[22] R. Shiavi, "Introduction to Applied Statistical Signal Analysis: Guide to Biomedical and Electrical Engineering Applications," pp. 314, Academic Press, 2007.

[23] Ç. Kocaman, M. Özdemir, "Comparison of Statistical Methods and Wavelet Energy Coefficients for Determining Two Common PQ Disturbances: Sag and Swell," in Proc. International Conference on Electrical and Electronics Engineering, Bursa, Turkey, Nov. 2009, pp. I-80 – I-84
[CrossRef]


[24] D. Rosca, "Wavelets on Two-dimensional Manifolds," pp. 8, Habilitation thesis, Technical University of Cluj-Napoca, 2012.

[25] I. Vujovic, J. soda, I. Kuzmanic, "Stabilising Illumination Variations in Motion Detection for Surveillance Applications," IET Image Processing, vol. 7, no. 7, pp. 671-678, 2013.
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 4]


[26] P. Rosin, E. Ioannidis, "Evaluation of Global Image Thresholding for Change Detection," Pattern Recognition Letters, vol. 24, no. 14, pp. 2345-2356, 2003.
[CrossRef] [Web of Science Times Cited 241] [SCOPUS Times Cited 304]


[27] D. K. Panda, S. Meher, "Detection of Moving Objects Using Fuzzy Color Difference Histogram Based Background Subtraction," IEEE Signal Processing Letters, vol. 23, no. 1, pp. 45-49, 2016.
[CrossRef] [Web of Science Times Cited 58] [SCOPUS Times Cited 70]


[28] S. Davarpanah, F. Khaid, L. Abdullah, "BGLBP-based Image Background Extraction Method," The International Arab Journal of Information Technology, vol. 13, no. 6A, pp. 908-914, 2016.

[29] H. Zhou, G. Su, X. Jiang, "Dynamic Foreground Detection Based on Improved Codebook Model," The Imaging Science Journal, vol. 64, no. 2, pp. 107-117, 2016.
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 5]


[30] W. Wang , N. Yang, Y. Zhang, F. Wang, T. Cao, P. Eklund, "A Review of Road Extraction from Remote Sensing Images," Journal of Traffic and Transportation Engineering, vol. 3, no. 3, pp. 271-282, 2016.
[CrossRef] [Web of Science Times Cited 208] [SCOPUS Times Cited 267]




References Weight

Web of Science® Citations for all references: 1,160 TCR
SCOPUS® Citations for all references: 1,483 TCR

Web of Science® Average Citations per reference: 37 ACR
SCOPUS® Average Citations per reference: 48 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 2024-12-05 17:17 in 162 seconds.




Note1: Web of Science® is a registered trademark of Clarivate Analytics.
Note2: SCOPUS® is a registered trademark of Elsevier B.V.
Disclaimer: All queries to the respective databases were made by using the DOI record of every reference (where available). Due to technical problems beyond our control, the information is not always accurate. Please use the CrossRef link to visit the respective publisher site.

Copyright ©2001-2024
Faculty of Electrical Engineering and Computer Science
Stefan cel Mare University of Suceava, Romania


All rights reserved: Advances in Electrical and Computer Engineering is a registered trademark of the Stefan cel Mare University of Suceava. No part of this publication may be reproduced, stored in a retrieval system, photocopied, recorded or archived, without the written permission from the Editor. When authors submit their papers for publication, they agree that the copyright for their article be transferred to the Faculty of Electrical Engineering and Computer Science, Stefan cel Mare University of Suceava, Romania, if and only if the articles are accepted for publication. The copyright covers the exclusive rights to reproduce and distribute the article, including reprints and translations.

Permission for other use: The copyright owner's consent does not extend to copying for general distribution, for promotion, for creating new works, or for resale. Specific written permission must be obtained from the Editor for such copying. Direct linking to files hosted on this website is strictly prohibited.

Disclaimer: Whilst every effort is made by the publishers and editorial board to see that no inaccurate or misleading data, opinions or statements appear in this journal, they wish to make it clear that all information and opinions formulated in the articles, as well as linguistic accuracy, are the sole responsibility of the author.




Website loading speed and performance optimization powered by: 


DNS Made Easy