Advertisement

The Feature Detection on the Homogeneous Surfaces with Projected Pattern

  • Paweł Popielski
  • Zygmunt Wróbel
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7339)

Abstract

In this article we deal with one of the fundamental problems in the area of the 3D reconstruction for objects with homogeneous surface such as, inter alia, human body or sculptures. The interest point detection on typical photos with many differing elements and changing intensities is already well-solved issue. Considerable difficulty and novelty is the interest point detection for homogeneous surfaces. To reconstruct such surfaces from images we have to artificially produce as many elements on surface as needed to allow proceed with the 3D coordinate’s extraction process with desired density. Four methods were selected. The first, definitely the best documented was the Harris corner detector. Next was the Nobel’s version of auto-correlation, the other was the minimum eigenvalue method known as the Kanade-Tomasi algorithm and the last tested method was the fast radial feature detector known as the Loy-Zelinsky algorithm. Chosen methods are well-known on the 3D reconstruction theatre, well implemented and documented, efficient in the terms of computational complexity. Also some image enhancements were utilized before feature extraction to improve the detection process. It was shown that the best choice was the Nobel’s version of auto-correlation function and a very interesting candidate for further research is the Loy-Zelinsky method.

Keywords

3D reconstruction photogrammetry feature detection pattern projection 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    D’apuzzo, N.: Automated Photogrammetric Measurement of Human Faces. Int. Archives of Photogrammetry and Remote Sensing XXXII, Part B5, 402–407 (1998)Google Scholar
  2. 2.
    D’apuzzo, N.: Measurement and modelling of human faces from multi images. Int. Archives of Photogrammetry and Remote Sensing 34(5), 241–246 (2002)Google Scholar
  3. 3.
    Chang, Y.: A Photogrammetric System for 3D Reconstruction of a Scoliotic Torso. A Master Thesis. Department of Geomatics Engineering, University of Calgary, Canada (2008)Google Scholar
  4. 4.
    Fischler, M.A., Bolles, R.C.: Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography. Comm. of the ACM 24, 381–395 (1981)CrossRefGoogle Scholar
  5. 5.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of Alvey Vision Conference, vol. 15, pp. 147–151 (1988)Google Scholar
  6. 6.
    Kraus, K.: Photogrammetry, vol. 1, pp. 277–279. Duemmler, Bonn (1993)Google Scholar
  7. 7.
    Lewis, J.P.: Fast normalized cross-correlation. In: Vision Interface, pp. 120–123 (1995)Google Scholar
  8. 8.
    Loy, G., Zelinsky, A.: Fast radial symmetry for detecting points of interest. IEEE PAMI 25(8), 959–973 (2003)CrossRefGoogle Scholar
  9. 9.
    Luong, Q.T., Faugeras, O.D.: The Fundamental Matrix: Theory, Algorithms, and Stability Analysis. International Journal of Computer Vision 17(1), 43–75 (1996)CrossRefGoogle Scholar
  10. 10.
    Malian, A., Azizi, A., Van Den Heuvel, F.A.: Medphos: A new photogrammetric system for medical measurement. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 35(B5), 311–316 (2004)Google Scholar
  11. 11.
    Mitchell, H.L.: Applications of digital photogrammetry to medical investigations. ISPRS Journal of Photogrammetry and Remote Sensing 50(3), 27–36 (1995)CrossRefGoogle Scholar
  12. 12.
    Mitchell, H.L., Newton, I.: Medical photogrammetric measurement: overview and prospects. ISPRS Journal of Photogrammetry and Remote Sensing 56(5-6), 286–294 (2002)CrossRefGoogle Scholar
  13. 13.
    Noble, A.: Descriptions of Image Surfaces. PhD thesis, Department of Engineering Science, Oxford University (1989)Google Scholar
  14. 14.
    Patias, P.: Medical imaging challenges photogrammetry. ISPRS Journal of Photogrammetry and Remote Sensing 56(5-6), 295–310 (2002)CrossRefGoogle Scholar
  15. 15.
    Popielski, P., Wróbel, Z.: An Attempt to Optimize the Process of Automatic Point Matching for Homogeneous Surface Objects. Archives of Photogrammetry, Cartography and Remote Sensing (2012) (manuscript submitted for publication)Google Scholar
  16. 16.
    Schenk, T.: Digital photogrammetry. TerraScience. Laurelville, Ohio, p. 428 (1999)Google Scholar
  17. 17.
    Schmid, C., Mohr, R., Bauckhage, C.: Evaluation of interest point detectors. International Journal of Computer Vision 37(2), 151–172 (2000)CrossRefGoogle Scholar
  18. 18.
    Shapiro, L., Stockman, G.C.: Computer Vision, p. 257. Prentice Books, Upper Saddle River (2001)Google Scholar
  19. 19.
    Shi, J., Tomasi, C.: Good Features to Track. In: 9th IEEE Conference on Computer Vision and Pattern Recognition. Springer (1994)Google Scholar
  20. 20.
    Tomasi, C., Kanade, T.: Detection and Tracking of Point Features. Pattern Recognition 37, 165–168 (2004)CrossRefGoogle Scholar
  21. 21.
    Zuliani, M., Kenney, C., Manjunath, B.S.: A Mathematical Comparison of Point Detectors. In: Second IEEE Image and Video Registration Workshop, IVR, Washington, DC (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Paweł Popielski
    • 1
  • Zygmunt Wróbel
    • 1
  1. 1.Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland

Personalised recommendations