|2/2017 - 9|
Wavelet Energy and the Usefulness of its Powers in Motion DetectionVUJOVIC, I. , KUZMANIC, I.
|View the paper record and citations in|
|Click to see author's profile in SCOPUS, IEEE Xplore, Web of Science|
|Download PDF (1,686 KB) | Citation | Downloads: 714 | Views: 2,292|
discrete wavelet transform, image motion analysis, morphological operations, motion detection, wavelet coefficients
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
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|
| 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]
 M. Seiferta, H. S. Hock, "The Independent Detection of Motion Energy and Counterchange: Flexibility in Motion Detection," Vision Research, vol. 98, pp. 6171, 2014.
[CrossRef] [Web of Science Times Cited 3] [SCOPUS Times Cited 4]
 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 202] [SCOPUS Times Cited 222]
 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]
 D. J. Joo, "Damage detection and system identification using a wavelet energy based approach," Columbia University, PhD thesis, 2012.
 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 98] [SCOPUS Times Cited 115]
 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 57]
 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]
 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. 698729, 2013.
[CrossRef] [Web of Science Times Cited 11] [SCOPUS Times Cited 11]
 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.
 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.
 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 27]
 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 7]
 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.
 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 17]
 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 11]
 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.
 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 49] [SCOPUS Times Cited 55]
 F. Trèves, "Topological Vector Spaces, Distributions and Kernels," pp. 95-126, Academic Press, 1995.
 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.
 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 178] [SCOPUS Times Cited 207]
 R. Shiavi, "Introduction to Applied Statistical Signal Analysis: Guide to Biomedical and Electrical Engineering Applications," pp. 314, Academic Press, 2007.
 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
 D. Rosca, "Wavelets on Two-dimensional Manifolds," pp. 8, Habilitation thesis, Technical University of Cluj-Napoca, 2012.
 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]
 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 236] [SCOPUS Times Cited 295]
 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 54] [SCOPUS Times Cited 64]
 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.
 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]
 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] [SCOPUS Times Cited 221]
Web of Science® Citations for all references: 904 TCR
SCOPUS® Citations for all references: 1,363 TCR
Web of Science® Average Citations per reference: 29 ACR
SCOPUS® Average Citations per reference: 44 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 2023-09-20 14:27 in 138 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.
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.