We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Reseach Article

Blind Image Separation based on a Flexible Parametric Distribution Function

by Nouf Saeed Al Otaibi
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 179 - Number 3
Year of Publication: 2017
Authors: Nouf Saeed Al Otaibi
10.5120/ijca2017915874

Nouf Saeed Al Otaibi . Blind Image Separation based on a Flexible Parametric Distribution Function. International Journal of Computer Applications. 179, 3 ( Dec 2017), 20-26. DOI=10.5120/ijca2017915874

@article{ 10.5120/ijca2017915874,
author = { Nouf Saeed Al Otaibi },
title = { Blind Image Separation based on a Flexible Parametric Distribution Function },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2017 },
volume = { 179 },
number = { 3 },
month = { Dec },
year = { 2017 },
issn = { 0975-8887 },
pages = { 20-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume179/number3/28717-2017915874/ },
doi = { 10.5120/ijca2017915874 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:54:20.941753+05:30
%A Nouf Saeed Al Otaibi
%T Blind Image Separation based on a Flexible Parametric Distribution Function
%J International Journal of Computer Applications
%@ 0975-8887
%V 179
%N 3
%P 20-26
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The blind image separation has been widely investigated nowadays. As a result, many algorithms of feature extraction have been developed for direct application of such image structures. One example of this, the separation of mixed fingerprints found in a crime scene, in which a mixture of two or more fingerprints may be gathered, for identification, they must be separated. In this paper, we propose a new technique for multiple mixed images separation based on modified Weibull distribution. We use an efficient method based on genetic algorithm and maximum likelihood for estimating the parameters of such score functions. Also the accuracy of this proposed distribution is measured, and we compare the algorithmic performance using the efficient approach with some other previous distributions. The numerical results show that the proposed distribution is flexible and has efficient results.

References
  1. Y. Zhang and Y. Zhao, 2013. Modulation domain blind speech separation in noisy environments, Speech Communication, vol. 55, no. 10, pp. 1081–1099.
  2. M. T. ¨ Ozgen, E. E. Kuruoˇglu, and D. Herranz, 2009. Astrophysical image separation by blind time-frequency source separation methods, Digital Signal Processing, vol. 19, no. 2, pp. 360–369.
  3. Ikhlef, K. Abed-Meraim, and D. Le Guennec, 2010. Blind signal separation and equalization with controlled delay for MIMO convolutive systems, Signal Processing, vol. 90, no. 9, pp. 2655– 2666.
  4. R. Romo V´azquez, H. V´elez-P´erez, R. Ranta, V. Louis Dorr, D. Maquin, and L. Maillard, Blind source separation, 2012. Wavelet denoising and discriminant analysis for EEG artifacts and noise cancelling, Biomedical Signal Processing and Control, vol. 7, no. 4, pp. 389–400.
  5. M. Babaie-Zadeh and C. Jutten, 2005. A general approach formutual information minimization and its application to blind source separation, Signal Processing, vol. 85, no. 5, pp. 975–995.
  6. K. Todros and J. Tabrikian, 2007. Blind separation of independent sources using Gaussian mixture model, IEEE Transactions on Signal Processing, vol. 55, no. 7, pp. 3645–3658.
  7. E. Oja and M. Plumbley, April 2003. Blind separation of positive sources using nonnegative PCA, in Proceedings of the 4th International Symposium on Independent Component Analysis and Blind Signal Separation (ICA ’03), Nara, Japan, pp. 11–16.
  8. M. Kuraya, A. Uchida, S. Yoshimori, and K. Umeno, 2008. Blind source separation of chaotic laser signals by independent component analysis, Optics Express, vol. 16, no. 2, pp. 725–730.
  9. P. Comon, 2014. Tensors: a brief introduction, IEEE Signal Processing Magazine, vol. 31, no. 2, pp. 44–53.
  10. W. L. Woo and S. S. Dlay, 2005. Neural network approach to blind signal separation of mono-nonlinearly mixed sources, IEEE Transactions on Circuits and Systems I, vol. 52, no. 6, pp. 1236–1247.
  11. Cichocki and R. Unbehauen, 1996. Robust neural networks with on-line learning for blind identification and blind separation of sources, IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, vol. 43, no. 11, pp. 894–906.
  12. S.-I. Amari, T.-P. Chen, and A. Cichocki, 1997. Stability analysis of learning algorithms for blind source separation, Neural Networks, vol. 10, no. 8, pp. 1345–1351.
  13. K. Kokkinakis and A. K. Nandi, 2005. Exponent parameter estimation for generalized Gaussian probability density functions with application to speech modeling, Signal Processing, vol. 85, no. 9, pp. 1852–1858.
  14. J. A. Palmer, K. Kreutz-Delgado, and S. Makeig, March 2006. Super-Gaussian mixture source model for ICA, in Proceedings of the International Conference on Independent Component Analysis and Blind Signal Separation, Charleston, SC, USA pp. 854–861.
  15. E. W. Stacy, 1962. A generalization of the gamma distribution, Annals of Mathematical Statistics, vol. 33, no. 3, pp. 1187–1192.
  16. Sarmiento, I. Durán-Díaz, A. Cichocki, and S. Cruces, 2015. A contrast based on generalized divergences for solving the permutation problem of convolved speech mixtures, IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 23, no. 11, pp. 1713-1726.
  17. J. Eriksson, J. Karvanen, and V. Koivunen, 2002. Blind separation methods based on Pearson system and its extensions, Signal Processing, vol. 82, no. 4, pp. 663–673.
  18. J. Karvanen, J. Eriksson, and V. Koivunen, 2002. Adaptive Score Functions for Maximum Likelihood ICA, The Journal of VLSI Signal Processing, Vol. 32, no 1-2, PP 83-92.
  19. J. Karvanen, J. Eriksson, and V. Koivunen, June 2000. Source distribution adaptive maximum likelihood estimation of ICA model, in Proceedings of the 2nd International Conference on ICA and BSS, Helsinki, Finland, pp. 227– 232.
  20. Aapo Hyvärinen and Erkki Oja, 2000, Independent Component Analysis: Algorithms and Applications, Neural Networks, vol.13, no. (4-5), pp. 411-430
  21. C. Jutten and J. Karhunen, 2004, “Advances in blind source separation (bss) and independent component analysis (ica) for nonlinear mixtures.”, Int J Neural Syst, vol. 14, no. 5, pp. 267–292.
  22. Mazen Zaindin, Ammar M. Sarhan, 2009, Parameters Estimation of the Modified Weibull Distribution, Applied Mathematical Sciences, Vol. 3, no. 11, pp. 541 – 550.
  23. M. Li and J. Mao, June 2004. A new algorithm of evolutional blind source separation based on genetic algorithm, in Proceedings of the 5th World Congress on Intelligent Control and Automation, Hangzhou, Zhejiang, China, pp. 2240–2244.
  24. S. Mavaddaty and A. Ebrahimzadeh, December 2009. Evaluation of performance of genetic algorithm for speech signals separation, in Proceedings of the International Conference on Advances in Computing, Control and Telecommunication Technologies (ACT’09), Trivandrum, Kerala, India, pp. 681–683.
  25. A. Hyvarinen, J. Karhunen, and E. Oja, 2001. Independent Component Analsysis, JohnWiley & Sons.
  26. Internet web: http://sipi.usc.edu/database/database.cgi
  27. Internet, web:http://www.lupusimages.com
Index Terms

Computer Science
Information Sciences

Keywords

Source separation blind image separation FastICA Maximum likelihood Genetic algorithm Modified Weibull distribution.