Skip to main content
SpringerLink
Log in

A completely affine invariant image-matching method based on perspective projection

  • Original Paper
  • Published:
Machine Vision and Applications Aims and scope Submit manuscript

Abstract

In many cases, feature-matching problems can boil down to the computation of affine invariant local image features. However, many methods which are used to obtain these image features are based on affine not perspective transformation. They typically fail to get enough matching points at extreme viewpoints. In this paper, a novel method based on perspective projection to simulate all image views by sampling a camera pose in 3D space is presented. Only four variables—three angles (pan, tilt and roll) and a scale factor which are used to describe the camera pose—are involved to represent an affine transformation matrix. All these samples generated by our method are more similar to the real images captured by camera than those generated by traditional methods. We demonstrate the performance and robustness of our method by comparing it with popular SIFT, ASIFT descriptors and randomized trees method. Experimental results show that a large range of viewpoints by learning the behavior of key points patterns can be handled even when the camera is placed at some extreme positions.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: Proceedings of International Conference on Computer Vision, pp. 525–531 (2001)

  2. Mikolajczyk K., Schmid C.: Scale and affine invariant interest point detectors. Int. J. Comput. Vis. 60, 63–86 (2004)

    Article  Google Scholar 

  3. Muse, P., Sur, F., Cao, F., Gousseau, Y.: Unsupervised thresholds for shape matching. In: Proceedings of the International Conference on Image Processing, pp. 647–650 (2003)

  4. Matas J., Chum O., Urban M., Pajdla T.: Robust wide-baseline stereo from maximally stable extremal regions. Imag. Vis. Comput. 22, 761–767 (2004)

    Article  Google Scholar 

  5. Lowe D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)

    Article  Google Scholar 

  6. Ozuysal, M., Calonder, M., Lepetit, V., Fua P.: Fast keypoint recognition using random ferns. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 448–461

  7. Calonder, M., Lepetit, V., Konolige, K., Mihelich, P., Bowman, J., Fua, P.: Compact signatures for high-speed interest point description and matching. In: Proceedings of 12th International Conference on Computer Vision (2009)

  8. Lepetit, V., Lagger, P., Fua, P.: Randomized trees for real-time keypoint recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 775–781 (2005)

  9. Florack L.M.J., Haar Romeny E.A.: General intensity transformations and differential invariants. J. Math. Imag. Vis. 4, 171–187 (1994)

    Article  Google Scholar 

  10. Mindru F., Tuytelaars T., Van Gool L., Moons T.: Moment invariants for recognition under changing viewpoint and illumination. Comput. Vis. Imag. Underst. 94, 3–27 (2004)

    Article  Google Scholar 

  11. Baumberg, A.: Reliable feature matching across widely separated views. In: Proceedings of Computer Vision and Pattern Recognition, pp. 774–781 (2000)

  12. Ke, Y., Sukthankar, R.: PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of Computer Vision and Pattern Recognition, pp. 506–513 (2004)

  13. Mikolajczyk K., Schmid C.: A performance evaluation of local descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 27(10), 1615–1630 (2005)

    Article  Google Scholar 

  14. Bay, H., Tuytelaars, T., Gool L.V.: Surf: speeded up robust features. In: Proceedings of European Conference on Computer Vision, pp. 404–417 (2006)

  15. Morel J.M., Yu G.: ASIFT: a new framework for fully affine invariant image comparison. SIAM J. Imag. Sci. 2(2), 438–469 (2009)

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Optoelectronics, Beijing Institute of Technology, No. 6 Building, Beijing, China

    Wei Liu, Yongtian Wang, Jing Chen & Junwei Guo

  2. School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China

    Yongtian Wang & Yang Lu

Authors
  1. Wei Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yongtian Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Junwei Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yang Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Wei Liu.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Liu, W., Wang, Y., Chen, J. et al. A completely affine invariant image-matching method based on perspective projection. Machine Vision and Applications 23, 231–242 (2012). https://doi.org/10.1007/s00138-011-0347-7

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00138-011-0347-7

Keywords

Search

Navigation