CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Image Processing Using Principal Component Analysis

by Pramod Kumar Pandey, Yaduvir Singh, Sweta Tripathi
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 15 - Number 4
Year of Publication: 2011
Authors: Pramod Kumar Pandey, Yaduvir Singh, Sweta Tripathi
10.5120/1935-2582

Pramod Kumar Pandey, Yaduvir Singh, Sweta Tripathi . Image Processing Using Principal Component Analysis. International Journal of Computer Applications. 15, 4 ( February 2011), 37-40. DOI=10.5120/1935-2582

@article{ 10.5120/1935-2582,
author = { Pramod Kumar Pandey, Yaduvir Singh, Sweta Tripathi },
title = { Image Processing Using Principal Component Analysis },
journal = { International Journal of Computer Applications },
issue_date = { February 2011 },
volume = { 15 },
number = { 4 },
month = { February },
year = { 2011 },
issn = { 0975-8887 },
pages = { 37-40 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume15/number4/1935-2582/ },
doi = { 10.5120/1935-2582 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:03:17.493602+05:30
%A Pramod Kumar Pandey
%A Yaduvir Singh
%A Sweta Tripathi
%T Image Processing Using Principal Component Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 15
%N 4
%P 37-40
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, a review on the latest methodologies and application of the Principle Component Analysis (PCA) has been done in the area of image processing. Exploring basic theory of multivariate analysis, which involves a mathematical procedure to transform a number of correlated variables into a number of uncorrelated variables have been studied, compared and analyzed for better performance. The PCA ultimately reduces the number of effective variables used for classification which are compared with some statistical method. A comparison is made to illustrate the important of PCA in various signal processing based application like Texture classification, Face recognition, Biometrics etc.

References
  1. Huiqi Li, Opus Chutatape, “Automatic Location of optic Disc in retinal Images,” IEEE Explore, pp. 837- 840, Oct 2008.
  2. Myoung Soo Park and Jin Hee Na et al, “pca-based feature extraction using class information”, IEEE International Conference on Systems, Man and Cybernetics, Vol.1, Oct. 2005, pp.341 – 345.
  3. J. Yang, D. Zhang, Alejandro F. Firangi, Jing-yu Yang, “Two-Dimensional PCA: A New Approach to appearance-Based Face Representation and Recognition,” IEEE transactions on pattern analysis and machine intelligence, vol. 26, no. 1, pp. 131-137, 2004
  4. A. Weingessel and K. Hornik, “Local PCA Algorithms,” IEEE transactions on neural networks, vol.11, no. 6, pp 1242-1250, Nov 2000.
  5. Alex Pentland, Baback Moghaddam, and Thad Starner, “View-Based and Modular Eigen spaces for Face Recognition”, IEEE Conf. on Computer Vision and Pattern Recognition, MIT Media Laboratory Tech. Report No. 245 1994.
  6. K.Hornik and C.M. Kuan, “Convergence Analysis of Local feature Extraction Algorithms,” Neural Networks, vol. 5, pp. 229-240, 1992.
  7. M. Turk and A. Pent land, “Eigenfaces for Recognition,” J. Cognitive Neuroscience, vol. 3, no. 1, pp. 71-86, 1991.
  8. M.A. Turk and A.P. Pent land, “Face Recognition Using Eigenfaces”, IEEE Conf. on Computer Vision and Pattern Recognition, pp. 586-591, 1991.
  9. F. Tomita, S. Tsuji, “Computer Analysis of Visual Textures,” Kulwer Academic, Boston, 1990.
  10. M. Kirby and L. Sirovich, “Application of the KL Procedure for the Characterization of Human Faces,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 12, no. 1, pp. 103-108, Jan. 1990.
  11. Wen Gao and Shiguang Shan, “Face Verification for Access Control,” chapter 13, pp. 138.
  12. L. Sirovich and M. Kirby, “Low-Dimensional Procedure for Characterization of Human Faces,” J. Optical Soc. Am., vol. 4, pp. 519-524, 1987.
  13. M.H. Bharati, John F. MacGregor, “Texture analysis of images using Principal Component Analysis,” McMaster University, Hamilton, Ont., Canada, L8S 4L7
Index Terms

Computer Science
Information Sciences

Keywords

Image Processing Component Analysis

Powered by PhDFocusTM