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Mathematical Problems in Engineering
Volume 2012 (2012), Article ID 382369, 14 pages
http://dx.doi.org/10.1155/2012/382369
Research Article

Multiscale Point Correspondence Using Feature Distribution and Frequency Domain Alignment

1School of Control Science and Engineering, Shandong University, Jinan 250061, China
2College of Information and Electrical Engineering, Shandong University of Science and Technology, Qingdao 266590, China
3State Key Lab of Intelligent Technologies and Systems, Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Automation, Tsinghua University, Beijing 100084, China

Received 23 July 2012; Revised 19 November 2012; Accepted 20 November 2012

Academic Editor: Asier Ibeas

Copyright © 2012 Zeng-Shun Zhao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. M. Z. Brown, D. Burschka, and G. D. Hager, “Advances in computational stereo,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 8, pp. 993–1008, 2003. View at Publisher · View at Google Scholar · View at Scopus
  2. Y. Bentoutou, N. Taleb, K. Kpalma, and J. Ronsin, “An automatic image registration for applications in remote sensing,” IEEE Transactions on Geoscience and Remote Sensing, vol. 43, no. 9, pp. 2127–2137, 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. P. Thévenaz, U. E. Ruttimann, and M. Unser, “A pyramid approach to subpixel registration based on intensity,” IEEE Transactions on Image Processing, vol. 7, no. 1, pp. 27–41, 1998. View at Scopus
  4. H. Foroosh, J. B. Zerubia, and M. Berthod, “Extension of phase correlation to subpixel registration,” IEEE Transactions on Image Processing, vol. 11, no. 3, pp. 188–200, 2002. View at Publisher · View at Google Scholar · View at Scopus
  5. B. S. Reddy and B. N. Chatterji, “An FFT-based technique for translation, rotation, and scale-invariant image registration,” IEEE Transactions on Image Processing, vol. 5, no. 8, pp. 1266–1271, 1996. View at Scopus
  6. K. Mikolajczyk and C. Schmid, “A performance evaluation of local descriptors,” in Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. II257–II263, June 2003. View at Scopus
  7. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” International Journal of Computer Vision, vol. 60, no. 2, pp. 91–110, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. R. Fergus, P. Perona, and A. Zisserman, “Object class recognition by unsupervised scale-invariant learning,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '03), pp. II 264–II 271, June 2003. View at Scopus
  9. H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, “SURF: speeded up robust features,” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346–359, 2008. View at Publisher · View at Google Scholar · View at Scopus
  10. S. Gauglitz, “Evaluation of interest point detectors and feature descriptors for visual tracking,” International Journal of Computer Vision, vol. 94, no. 3, pp. 335–360, 2011.
  11. R. N. Bracewell, K. Y. Chang, A. K. Jha, and Y. H. Wang, “Affine theorem for two-dimensional Fourier transform,” Electronics Letters, vol. 29, no. 3, article 304, 1993. View at Scopus
  12. G. Hager and K. Toyama, “Xvision: combining image warping and geometric constraints for fast visual tracking,” in Proceedings of the 4th European Conference on Computer Vision, pp. 507–517, 1996.
  13. Y. Keller, A. Averbuch, and M. Israeli, “Pseudopolar-based estimation of large translations, rotations, and scalings in images,” IEEE Transactions on Image Processing, vol. 14, no. 1, pp. 12–22, 2005. View at Publisher · View at Google Scholar
  14. P. Vandewalle, S. Süsstrunk, and M. Vetterll, “A frequency domain approach to registration of aliased images with application to super-resolution,” Eurasip Journal on Applied Signal Processing, vol. 2006, p. 233, 2006. View at Publisher · View at Google Scholar · View at Scopus
  15. R. Szeliski and J. Coughlan, “Hierarchical spline-based image registration,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 194–201, June 1994. View at Scopus
  16. A. A. Cole-Rhodes, K. L. Johnson, J. Lemoigne, et al., “Multiresolution registration of remote sensing imagery by optimization of mutual information using a stochastic gradient,” IEEE Transactions on Image Processing, vol. 12, no. 12, pp. 1495–1511, 2003.
  17. M. A. Fischler and R. C. Bolles, “Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography,” Communications of the Association for Computing Machinery, vol. 24, no. 6, pp. 381–395, 1981. View at Publisher · View at Google Scholar
  18. Z.-S. Zhao, Q.-J. Tian, J.-Z. Wang, and J.-M. Zhou, “Image match using distribution of colorful SIFT,” in International Conference on Wavelet Analysis and Pattern Recognition (ICWAPR '10), pp. 150–153, 2010.