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Image Registration using Discrete Cosine Transform and Normalized Cross Correlation

Published on March 2012 by Ruhina B. Karani, Tanuja K. Sarode
International Conference and Workshop on Emerging Trends in Technology
Foundation of Computer Science USA
ICWET2012 - Number 2
March 2012
Authors: Ruhina B. Karani, Tanuja K. Sarode
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Ruhina B. Karani, Tanuja K. Sarode . Image Registration using Discrete Cosine Transform and Normalized Cross Correlation. International Conference and Workshop on Emerging Trends in Technology. ICWET2012, 2 (March 2012), 28-34.

@article{
author = { Ruhina B. Karani, Tanuja K. Sarode },
title = { Image Registration using Discrete Cosine Transform and Normalized Cross Correlation },
journal = { International Conference and Workshop on Emerging Trends in Technology },
issue_date = { March 2012 },
volume = { ICWET2012 },
number = { 2 },
month = { March },
year = { 2012 },
issn = 0975-8887,
pages = { 28-34 },
numpages = 7,
url = { /proceedings/icwet2012/number2/5323-1014/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and Workshop on Emerging Trends in Technology
%A Ruhina B. Karani
%A Tanuja K. Sarode
%T Image Registration using Discrete Cosine Transform and Normalized Cross Correlation
%J International Conference and Workshop on Emerging Trends in Technology
%@ 0975-8887
%V ICWET2012
%N 2
%P 28-34
%D 2012
%I International Journal of Computer Applications
Abstract

In recent years, the accelerated growth in the field of computer vision, image fusion, medical imaging, military automatic target recognition, remote cartography and astrophotography has established the need for the development of good image registration technique for the efficient retrieval of interest point area. Image registration is the process of geometrically aligning one image to another image of the same scene taken from different viewpoints or by different sensors. The idea is to transform different sets of data into one coordinate system. It is a fundamental image processing technique and is very useful inintegrating information from different sensors, finding changes in images taken at different timesand inferring three-dimensional information from stereo images. Registration involves finding out area of interest by comparing the unregistered image with source image and finding the part that has highest similarity matching.This paper presents the image registration techniques based on extracting interest point area of satellite images using Discrete Cosine Transform and normalized cross correlation. The proposed algorithm is worked over various sizes of satellite images such as 256X256, 1024X1024 etc. The root mean square error is used as similarity measure. The experiment results show that the proposed algorithm can successfully process local distortion in high-resolution satellite images. The comparative study shows that DCT is faster and gives more accurate results.

References
  1. Zhen Xiong and Yun Zhang “A Novel Interest-Point-Matching Algorithm for High-Resolution Satellite Images” ieee transactions on geoscience and remote sensing, vol. 47, no. 12, december 2009.
  2. Gang Hong, Yun Zhang “Combination of Feature-based and Area- based Image Registration Technique for High Resolution Remote Sensing Image” ieee international conference on Geoscience and Remote Sensing Symposium, 2007.
  3. Ronald W. K. So and Albert C. S. Chung “Multi-level non-rigid image registration using graph-cuts”ieee international conference on Acoustics, Speech and Signal Processing, 2009.
  4. M. Auer, P. Regitnig, and G. A. Holzapfel, “An automatic non rigid registration for stained histological sections,” IEEE Trans. Image Process.,vol. 14, no. 4, pp. 475–486, Apr. 2005.
  5. Goshtasby, A., “Piecewise linear mapping functions for image registration, Pattern Recognition”, vol.19, no.6, pp.459-466,1986.
  6. Dongjin Kwon, “Rolled Fingerprint Construction Using MRF-Based Nonrigid Image Registration”, ieee transactions on image processing, vol. 19, no. 12, december 2010
  7. Hongjun Jia, Guorong Wu, Qian Wang, Minjeong Kim, and Dinggang Shen “Itree: fast and accurate image registration based on the combinative and incremental tree” ” ieee
  8. international conference on Biomedical Imaging: From Nano to Macro, 2011
  9. Boffy, Y. Tsin, and Y. Genc, “Real-time feature matching using adaptive and spatially distributed classification trees,” Brit. Mach. Vis. Conf., Jul. 2006.
  10. Ming-Sui Lee, Meiyin Shen, Akio Yoneyama and C. -C. Jay Kuo “DCT-Domain Image Registration Techniques for Compressed Video” ieee international conference on circuits and systems, 2005.
  11. L. G. Brown, “A survey of image registration techniques,” ACM Comput.Surv., vol. 24, no. 4, pp. 325–376, Dec. 1992.
  12. Dr. H.B.Kekre, Sudeep D. Thepade, Akshay M aloo, “Image Retrieval using Fractional Coefficients of Transformed Image using DCT and Walsh Transform”, International Journal of Engineering Science and Technology , Vol. 2(4), pp .362-371, 2010.
  13. H. Chui and A. Rangarajan, “A new point matching algorithm for nonrigid registration,” Comput. Vis. Image Underst., vol. 89, no. 2/3, pp. 114– 141, Feb. 2003.
  14. C. Harris and M. Stephens, “A combined corner and edge detector,” in Proc. Alvey Vis. Conf., 1988, pp. 147–151.
  15. G. K. Wallace, “Overview of the JPEG still Image Compression standard,” SPIE 1244 (1990) 220-233.
  16. J. Rexilius, S. K. Warfield, C. R. G. Guttmann, X. Wei, R. Benson,L. Wolfson, M. Shenton, H. Handels, and R. Kikinis,“A novel nonrigidregistration algorithm and applications,” in Proc.MICCAI, W. Niessen and M. Viergever, Eds., 2001, vol. 2208, pp. 923–931.
  17. J. Williams and M. Bennamoun, “Simultaneous registration of multiplecorresponding point sets,” Comput. Vis. Image Underst., vol. 81, no. 1, pp. 117–142, Jan. 2001.
  18. Sang-M i Lee, Hee_Jung Bae, and Sung-Hwan Jung, “Efficient Content-Based Image Retrieval M ethods Using Color and Texture”, ETRI Journal 20 (1998) 272-283.
  19. Sahil Suri and Peter Reinartz, “Mutual-Information-Based Registration of TerraSAR-X and Ikonos Imagery in Urban Areas” ieee transactions on geoscience and remote sensing, vol. 48, no. 2, february 2010
  20. Mingchao Sun, Bao Zhang, linghong Liu, Yongyang Wang and Quan Yang,” The Registration of Aerial Infrared and Visible Images” International Coriference on Educational and Information Technology 2010.
  21. L. Zagorchev, and A. Goshtasby, “A comparative study of transformation functions for nonrigid image registration,” IEEE Transactions on Image Processing, vol.15, pp. 529-538, 2006.
Index Terms

Computer Science
Information Sciences

Keywords

Discrete Cosine Transform (DCT) Normalized Cross Correlation Interst Point Area Extraction Image Registration