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Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 1848471, 9 pages
http://dx.doi.org/10.1155/2016/1848471
Research Article

Target Matching Recognition for Satellite Images Based on the Improved FREAK Algorithm

1Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
2University of Chinese Academy of Sciences, Beijing 100049, China

Received 4 June 2016; Accepted 7 September 2016

Academic Editor: Yakov Strelniker

Copyright © 2016 Yantong Chen 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. S. Korman, D. Reichman, G. Tsur, and S. Avidan, “FasT-match: fast affine template matching,” in Proceedings of the 26th IEEE Conference on Computer Vision and Pattern Recognition (CVPR '13), pp. 2331–2338, Portland, Ore, USA, June 2013. View at Publisher · View at Google Scholar · View at Scopus
  2. H. Jiang, T.-P. Tian, K. He, and S. Sclaroff, “Scale resilient, rotation invariant articulated object matching,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR '12), pp. 143–150, IEEE, Providence, RI, USA, June 2012. View at Publisher · View at Google Scholar · View at Scopus
  3. J. C. Yang and D. S. Park, “A fingerprint verification algorithm using tessellated invariant moment features,” Neurocomputing, vol. 71, no. 10–12, pp. 1939–1946, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. C. Cyr and B. Kimia, “3D object recognition using similarity-based aspect graph,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (ICCV '01), pp. 254–261, IEEE, Vancouver, Canada, July 2001.
  5. V. Deepu and S. Madhvanath, “Principal component analysis for online handwritten character recognition,” Pattern Recognition, vol. 2, no. 1, pp. 327–330, 2004. View at Google Scholar
  6. N. Otsu, “A threshold selection method from gray-level histograms,” Systems, Man, and Cybernetics, vol. 9, no. 1, pp. 62–66, 1979. View at Publisher · View at Google Scholar
  7. Q. Chen, “A comparative study of Fourier descriptors and Hu’s seven moment invariants for image recognition,” Electrical and Computer Engineering, vol. 1, no. 5, pp. 103–106, 2004. View at Google Scholar
  8. 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
  9. H. Bay, A. Ess, and T. Tuytelaars, “A Ess, and T Tuytelaars, SURF: speeded up robust features,” Computer Vision and Image Understanding, vol. 110, no. 3, pp. 346–359, 2008. View at Google Scholar
  10. Y. Ke and R. Sukthankar, “PCA-SIFT: a more distinctive representation for local image descriptors,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '04), pp. II506–II513, San Diego, Calif, USA, July 2004. View at Scopus
  11. K. Mikolajczyk and C. Schmid, “A performance evaluation of local descriptors,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 10, pp. 1615–1630, 2005. View at Publisher · View at Google Scholar · View at Scopus
  12. E. Tola, V. Lepetit, and P. Fua, “DAISY: an efficient dense descriptor applied to wide-baseline stereo,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 32, no. 5, pp. 815–830, 2010. View at Publisher · View at Google Scholar · View at Scopus
  13. M. Calonder, V. Lepetit, and C. Strecha, “BRIEF: binary robust independent elementary features,” in Proceedings of the European Conference on Computer Vision (ECCV '10), pp. 778–792, Crete, Greece, 2010.
  14. A. Alahi and E. Polytech, “FREAK: fast retina keypoint,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR '12), pp. 510–517, IEEE, Providence, RI, USA, June 2012.
  15. T. Lindeberg, “Feature detection with automatic scale selection,” International Journal of Computer Vision, vol. 30, no. 2, pp. 79–116, 1998. View at Publisher · View at Google Scholar · View at Scopus
  16. D. Serby, Esther-Koller-Meier, and L. Van Gool, “Probabilistic object tracking using multiple features,” in Proceedings of the 17th International Conference on Pattern Recognition (ICPR '04), pp. 184–187, August 2004. View at Scopus
  17. H.-J. Lee and J.-W. Chang, “Signature-based hybrid spill-tree for indexing high-dimensional data,” in Proceedings of the IEEE 9th International Conference on Computer and Information Technology (CIT '09), pp. 287–292, IEEE, Xiamen, China, October 2009. View at Publisher · View at Google Scholar · View at Scopus