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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: Joseph N. Mait
  • Vol. 52, Iss. 1 — Jan. 1, 2013
  • pp: 96–104

Invariant matching method for different viewpoint angle images

Min Chen, Zhenfeng Shao, Dongyang Li, and Jun Liu  »View Author Affiliations


Applied Optics, Vol. 52, Issue 1, pp. 96-104 (2013)
http://dx.doi.org/10.1364/AO.52.000096


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Abstract

In recent years, many methods have been put forward to improve the image matching for different viewpoint images. However, these methods are still not able to achieve stable results, especially when large variation in view occurs. In this paper, an image matching method based on affine transformation of local image areas is proposed. First, local stable regions are extracted from the reference image and the test image, and transformed to circular areas according to the second-order moment. Then, scale invariant features are detected and matched in the transformed regions. Finally, we use epipolar constraint based on the fundamental matrix to eliminate wrong corresponding pairs. The goal of our method is not to increase the invariance of the detector but to improve the final performance of the matching results. The experimental results demonstrate that compared with the traditional detectors the proposed method provides significant improvement in robustness for different viewpoint images matching in the 2D scene and 3D scene. Moreover, the efficiency is greatly improved compared with affine scale invariant feature transform (Affine-SIFT).

© 2012 Optical Society of America

OCIS Codes
(100.2000) Image processing : Digital image processing
(150.0150) Machine vision : Machine vision

ToC Category:
Image Processing

History
Original Manuscript: March 20, 2012
Revised Manuscript: October 17, 2012
Manuscript Accepted: November 19, 2012
Published: December 21, 2012

Citation
Min Chen, Zhenfeng Shao, Dongyang Li, and Jun Liu, "Invariant matching method for different viewpoint angle images," Appl. Opt. 52, 96-104 (2013)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-52-1-96


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References

  1. S. N. Sinha, J-M. Frahm, M. Pollefeys, and Y. Genc, “Feature tracking and matching in video using programmable graphics hardware,” Mach. Vis. Appl. 22, 207–217 (2011). [CrossRef]
  2. M. Brown and D. G. Lowe, “Automatic panoramic image stitching using invariant features,” Int. J. Comput. Vis. 74, 59–73 (2007). [CrossRef]
  3. B. E. Kratochvil, L. X. Dong, L. Zhang, and B. J. Nelson, “Image-based 3D reconstruction using helical nanobelts for localized rotations,” J. Microsc. 237, 122–135 (2010). [CrossRef]
  4. C. Harris and M. Stephens, “A combined corner and edge detector,” in Proceedings of the 4th Alvey Vision Conference (Plessey, 1988), pp. 147–152.
  5. K. Mikolajczyk and C. Schmid, “Scale and affine invariant interest point detectors,” Int. J. Comput. Vis. 60, 63–86 (2004). [CrossRef]
  6. S. Smith and J. Brady, “Susan: a new approach to low-level image-processing,” Int. J. Comput. Vis. 23, 45–78 (1997). [CrossRef]
  7. D. G. Lowe, “Distinctive image features from scale-invariant keypoints,” Int. J. Comput. Vis. 60, 91–110 (2004). [CrossRef]
  8. H. Bay, A. Ess, T. Tuytelaars, and L. V. Gool, “Speeded-up robust features (SURF),” Comput. Vis. Image Underst. 110, 346–359 (2008). [CrossRef]
  9. K. Mikolajczyk, “Interest point detection invariant to affine transformations,” Ph.D dissertation (Institut National Polytechnique de Grenoble, 2002).
  10. J. Matas, O. Chum, M. Urban, and T. Pajdla, “Robust wide-baseline stereo from maximally stable extremal regions,” Image Vis. Comput. 22, 761–767 (2004). [CrossRef]
  11. T. Tuytelaars and L. V. Gool, “Matching widely separated views based on affine invariant regions,” Int. J. Comput. Vis. 59, 61–85 (2004). [CrossRef]
  12. N. Dalal and B. Triggs, “Histograms of oriented gradients for human detection,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2005), pp. 886–893.
  13. A. Barla, F. Odone, and A. Verri, “Histogram intersection kernel for image classification,” in Proceedings of IEEE International Conference on Image Processing (IEEE, 2003), pp. III-513-16.
  14. J. M. Morel and G. Yu, “ASIFT: a new framework for fully affine invariant image comparison,” SIAM J. Imaging Sci. 2, 1–31 (2009). [CrossRef]
  15. G. Yu and J. M. Morel, “A fully affine invariant image comparison method,” in Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (IEEE, 2009), pp. 1597–1600.
  16. Y. Yu, K. Huang, W. Chen, and T. Tan, “A novel algorithm for view and illumination invariant image matching,” IEEE Trans. Image Processing 21, 229–240 (2012). [CrossRef]
  17. A. Baumberg, “Reliable feature matching across widely separated views,” in Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (IEEE, 2000), pp. 774–781.
  18. K. Mikolajczyk, T. Tuytelaars, C. Schmid, A. Zisserman, J. Matas, F. Schaffalitzky, T. Kadir, and L. V. Gool, “A comparison of affine region detectors,” Int. J. Comput. Vis. 65, 43–72 (2005). [CrossRef]
  19. P.-E. Forssen and D. G. Lowe, “Shape descriptors for maximally stable extremal regions,” in Proceedings of IEEE International Conference on Computer Vision (IEEE, 2007), pp. 1–8.
  20. R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision (Cambridge University, 2000).
  21. http://www.robots.ox.ac.uk/~vgg/research/affine .
  22. http://www.ipol.im/pub/algo/my_affine_sift/ .
  23. http://cmp.felk.cvut.cz/~wbsdemo/demo/ .

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