Document Type : Original Article
Authors
Electrical, RGPV/MITS, Gwalior, M.P, India
Abstract
The principle of dimensionality reduction with PCA is the representation of the dataset ‘X’in terms of eigenvectors ei ∈ RN of its covariance matrix. The eigenvectors oriented in the direction with the maximum variance of X in RN carry the most relevant information of X. These eigenvectors are called principal components [8]. Assume that n images in a set are originally represented in matrix form as Ui∈ Rr ×c, i = 1,......,n, where r and c are, repetitively, the number of rows and columns of the matrix. In vectorized representation (matrix-to-vector alignment) each Ui is a N = r × c- dimensional vector ai computed by sequentially concatenating all of the lines of the matrix Ui. To compute the Principal Components the covariance matrix of U is formed and Eigen values, with the corresponding eigenvectors, are evaluated. The Eigen vectors forms a set of linearly independent vectors, i.e., the base {φ} n i=1 which consist of a new axis system [10]
Keywords
[2]. Zhengya Xu, Hong Ren, Xinghuo “Color Image Enhancement by Virtual Histogram Approach” IEEEjournal consumer electronics, vol.-56, NO. 2, may 2010, pp.204-208. Subramanya A, “Image Compression Technique,” Potentials IEEE, Vol. 20, Issue 1, pp 19-23, Feb2011.
[3]. David Jeff Jackson & Sidney Joel Hannah, “ Comparative Analysis of image Compression Techniques,” System Theory 1993, Proceedings SSST ’93, 25th Southeastern Symposium,pp 513-517, 7 –9 March 1993.
[4]. Hong Zhang, Xiaofei Zhang & Shun Cao, “ Analysis & Evaluation of Some Image Compression Techniques,” High Performance Computing in AsiaPacific Region, 2000 Proceedings, 4th Int. Conference, vol. 2, pp 799-803,14-17 May, 2000.
[5]. Maryam Amirmazlaghani, Hamidreza Amindavar, “A Novelwavelet Domain
Statistical Approach for Denoising SAR Images” ICIP, 2009.
Sample paper of International journal of Innovation and Applied Studies
Saraswat et al Int J Adv Biol Biom Res. 2013; 1(7):767-776 ISSN: 2028-9324 Vol. X No. X, Month 20XX 776 | Page
[6]. Y.MuraliMohanBabu, M.V.Subramanyam & M.N.Giriprasad “Bayesian Denoising of SAR Image”
International Journal of Computer Science & Technology, Vol.2, Issue.1, PP-72-74, 2011,
[7]. C. Boncelet, “Image noise models,” in Handbook of Image and Video Processing, A. C. Bovik, Ed.New York: Academic, 2000.
[8]. S. Mallat, "A Wavelet Tour of Signal Processing", 2nd ed. San Diego, CA: Academic, 1999.
[9]. J. K. Romberg, H. Choi, R. G. Baraniuk, “Bayesian tree-structured image modeling using waveletdomainhidden Markov models,” IEEETrans. Image Process., Vol. 10, No. 7, pp. 1056–1068, Jul.2001.
[10]. K. John Peter , Dr K.Senthamarai Kannan , Dr S. Arumugan , G.Nagarajan, “Twostage image denoising by Principal Component Analysis with Self Similarity pixel Strategy”, International Journal of Computer Science and Network Security, VOL.11 No.5, May 2011, 296-301.
[11]. Ayan Chakrabarti and Keigo Hirakawa, “Effective separation of sparse and non-sparse image feature for denoising”, ICASSP 2008, 857-860.
[12]. P. Cosman, R. Gray, “Evaluating Quality of Compressed Medical Images: SNR, Subjective Rating, and Diagnostic Accurac”, in Proc. IEEE, Vol. 82, 1994, pp. 919-932.
)