Advertisement

Corner Detection in Spherical Images via the Accelerated Segment Test on a Geodesic Grid

  • Hao Guan
  • William A. P. Smith
  • Peng Ren
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8033)

Abstract

We extend the Accelerated Segment Test (AST) corner detector to operate on spherical images. We represent images using a discrete geodesic grid composed of triangular or hexagonal pixels. This representation has a number of advantages over the more common equirectangular parameterisation and, in the case of hexagonal pixels, leads more naturally to the discrete circular discs used in the AST. We present results on fully spherical imagery and show that our spherical AST outperforms planar AST applied to equirectangular images in terms of repeatability under rotation.

Keywords

Hexagonal Lattice Triangular Lattice Scale Invariant Feature Transform Corner Detection Spherical Image 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Sahr, K., White, D., Kimerling, A.J.: Geodesic discrete global grid systems. Cartography and Geographic Information Science 30, 121–134 (2003)CrossRefGoogle Scholar
  2. 2.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Alvey Vision Conference, Manchester, UK, vol. 15, p. 50 (1988)Google Scholar
  3. 3.
    Noble, A.: Descriptions of Image Surfaces (Ph.D.). Department of Engineering Science, Oxford University, 45 (1989)Google Scholar
  4. 4.
    Smith, S.M., Brady, J.M.: Susan—a new approach to low level image processing. International Journal of Computer Vision 23, 45–78 (1997)CrossRefGoogle Scholar
  5. 5.
    Rosten, E., Porter, R., Drummond, T.: Faster and better: A machine learning approach to corner detection. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 105–119 (2010)CrossRefGoogle Scholar
  6. 6.
    Mair, E., Hager, G.D., Burschka, D., Suppa, M., Hirzinger, G.: Adaptive and generic corner detection based on the accelerated segment test. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part II. LNCS, vol. 6312, pp. 183–196. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Arican, Z., Frossard, P.: Sampling-aware polar descriptors on the sphere. In: 2010 17th IEEE International Conference on Image Processing (ICIP), pp. 3509–3512. IEEE (2010)Google Scholar
  8. 8.
    Cruz-Mota, J., Bogdanova, I., Paquier, B., Bierlaire, M., Thiran, J.P.: Scale invariant feature transform on the sphere: Theory and applications. International Journal of Computer Vision 98, 217–241 (2012)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60, 91–110 (2004)CrossRefGoogle Scholar
  10. 10.
    Middleton, L., Sivaswamy, J.: Hexagonal Image Processing. Springer (2005)Google Scholar
  11. 11.
    Liu, S.J., Coleman, S., Kerr, D., Scotney, B., Gardiner, B.: Corner detection on hexagonal pixel based images. In: 2011 18th IEEE International Conference on Image Processing (ICIP), pp. 1025–1028. IEEE (2011)Google Scholar
  12. 12.
    de Berg, M., van Kreveld, M., Overmars, M., Schwarzkopf, O.: Computational Geometry: Algorithms and Applications. Springer, Berlin (1997)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Hao Guan
    • 1
  • William A. P. Smith
    • 1
  • Peng Ren
    • 2
  1. 1.Department of Computer ScienceThe University of YorkUK
  2. 2.College of Information and Control EngineeringChina University of Petroluem (Huadong)China

Personalised recommendations