Advertisement

The Effectiveness of Matching Methods for Rectified Images

  • Pawel PopielskiEmail author
  • Zygmunt Wrobel
  • Robert Koprowski
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 226)

Abstract

Medical diagnostics today is based mainly on invasive methods and it should be strongly emphasised that they include not only the X-ray imaging, but also CT and MRI scanning. For several years in various research centres, there have been attempts to create a non-invasive medical diagnostic systems based on the fusion of photogrammetric and computer vision methods. Both the complexity of the problem and commitment to used well-known methods of diagnosis in medical circles did not allow for the creation of a fully functional prototype of system that could be implemented. In the paper, the authors present the problem of 3D reconstruction with a diagnosis of suitability of various matching methods used for rectified images. The result clearly indicate the superiority of the algorithm based on variational solution. The authors in their work on the development of photogrammetric non-invasive medical diagnostic system have not come across such an analysis. Therefore, they concluded that presenting such an analysis will be useful in further research.

Keywords

Image Space Camera Calibration Left Image Epipolar Line Epipolar Geometry 
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.
    Daniilidis, K., Klette, R.: Imaging Beyond the Pinhole Camera”, Computational Imaging and Vision, vol. 33. Springer (2006)Google Scholar
  2. 2.
    D’apuzzo, N.: Automated Photogrammetric Measurement of Human Faces. In: Int. Archives of Photogrammetry and Remote Sensing, Hakodate, Japan, vol. XXXII, Part B5, pp. 402–407 (1998)Google Scholar
  3. 3.
    D’apuzzo, N.: Measurement and modelling of human faces from multi images. International Archives of Photogrammetry and Remote Sensing 34(5), 241–246 (2002)Google Scholar
  4. 4.
    Bailey, D., Borwein, J., Mattingly, A., Wightwick, G.: The Computation of Previously Inaccessible Digits of and Catalan’s Constant, Notices of the American Mathematical Society (2011), http://crd.lbl.gov/~dhbailey/dhbpapers/bbp-bluegene.pdf (accessed April 15, 2011)
  5. 5.
    Berggren, L., Borwein, J.M., Borwein, P.B.: Pi: a Source Book. Springer, New York (2004)Google Scholar
  6. 6.
    Bouguet, J-Y.: Camera calibration toolbox for Matlab, http://www.vision.caltech.edu/bouguetj/calib_doc/index.html
  7. 7.
    Brown, D.C.: Decentering Distortion of Lenses. Photometric Engineering 32, 444–462 (1966)Google Scholar
  8. 8.
    Brown, D.C.: Close-range camera calibration. Photogrammetric Engineering 37, 855–866 (1971)Google Scholar
  9. 9.
    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
  10. 10.
    Cyganek, B., Siebert, J.: An Introduction to 3D Computer Vision Techniques and Algorithms. Willey (2009)Google Scholar
  11. 11.
    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)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Forsyth, D., Ponce, J.: Computer Vision: A Modern Approach. Prentice-Hall (2003)Google Scholar
  13. 13.
    Fryer, J.G., Brown, D.C.: Lens distortion for close-range photogrammetry. Photogrammetric Engineering and Remote Sensing 52, 51–58 (1986)Google Scholar
  14. 14.
    Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of Alvey Vision Conference, vol. 15, pp. 147–151 (1988)Google Scholar
  15. 15.
    Hartley, R., Zisserman, A.: Multiple View Geometry in Computer Vision. Cambridge University Press (2006)Google Scholar
  16. 16.
    Heikkila, J., Silven, O.: A four-step camera calibration procedure with implicit image correction. In: Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition, p. 1106 (1997)Google Scholar
  17. 17.
    Hirschmuller, H.: Stereo Processing by Semiglobal Matching and Mutual Information. IEEE Transactions on Pattern Analysis and Machine Intelligence 30 (2008)Google Scholar
  18. 18.
    Konolige, K.: Small vision system: Hardware and implementation. In: Proceedings of the International Symposium on Robotics Research, Hayama, Japan, pp. 111–116 (1997)Google Scholar
  19. 19.
    Korzynska, A., Iwanowski, M.: Multistage morphological segmentation of brightfield and fluorescent microscopy images. Opto-Electronics Review 20(2), 174–186 (2012)CrossRefGoogle Scholar
  20. 20.
    Korzynska, A., Hoppe, A., Strojny, W., et al.: Investigation of a combined texture and contour method for segmentation of light microscopy cell images. In: Proceedings of the Second IASTED International Conference on Biomedical Engineering 2004, pp. 234–239 (2004)Google Scholar
  21. 21.
    Kosov, S., Thormählen, T., Seidel, H.-P.: Accurate Real-Time Disparity Estimation with Variational Methods. In: 5th International Symposium on Visual Computing, USA (2009)Google Scholar
  22. 22.
    Kraus, K.: Photogrammetry. Walter de Gruyter, Berlin (2007)CrossRefGoogle Scholar
  23. 23.
    Lewis, J.P.: Fast normalized cross-correlation. Vision Interface, 120–123 (1995)Google Scholar
  24. 24.
    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
  25. 25.
    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
  26. 26.
    Mitchell, H.L.: Applications of digital photogrammetry to medical investigations. ISPRS Journal of Photogrammetry and Remote Sensing 50(3), 27–36 (1995)CrossRefGoogle Scholar
  27. 27.
    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
  28. 28.
    Noble, A.: Descriptions of Image Surfaces, PhD thesis, Department of Engineering Science, Oxford University (1989)Google Scholar
  29. 29.
    Patias, P.: Medical imaging challenges photogrammetry. ISPRS Journal of Photogrammetry and Remote Sensing 56(5-6), 295–310 (2002)CrossRefGoogle Scholar
  30. 30.
    Popielski, P., Wróbel, Z.: The feature detection on the homogeneous surfaces with projected pattern. In: Piętka, E., Kawa, J. (eds.) ITIB 2012. LNCS, vol. 7339, pp. 118–128. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  31. 31.
    Porwik, P., Para, T.: Some handwritten signature parameters in biometric recognition process. In: Proceedings of the ITI 2007 29th International Conference on Information Technology Interfaces Book Series: ITI 2007, pp. 185–190 (2007)Google Scholar
  32. 32.
    Porwik, P., Wrobel, K., Doroz, R.: The Polish Coins Denomination Counting by Using Oriented Circular Hough Transform. In: Kurzynski, M., Wozniak, M. (eds.) Computer Recognition Systems 3. AISC, vol. 57, pp. 569–576. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  33. 33.
    Schenk, T.: Digital photogrammetry, TerraScience, Laurelville, Ohio, 428 (1999)Google Scholar
  34. 34.
    Shapiro, L., Stockman, G.C.: Computer Vision. Prentice-Hall (2002)Google Scholar
  35. 35.
    Wróbel, K., Doroz, R.: The new method of signature recognition based on least squares contour alignment. In: International Conference on Biometrics and Kansei Engineering, pp. 80–83 (2009)Google Scholar
  36. 36.
    Zhang, Z.: Flexible camera calibration by viewing a plane from unknown orientations. In: Proceedings of the 7th International Conference on Computer Vision, Corfu, pp. 666–673 (1999)Google Scholar
  37. 37.
    Zhang, Z.: A flexible new technique for camera calibration. IEEE Transactions on Pattern Analysis and Machine Intelligence 22, 1330–1334 (2000)CrossRefGoogle Scholar
  38. 38.
    Kajan, E.: Information technology encyclopedia and acronyms. Springer, Heidelberg (2002)zbMATHCrossRefGoogle Scholar
  39. 39.
    Broy, M.: Software engineering – From auxiliary to key technologies. In: Broy, M., Denert, E. (eds.) Software Pioneers. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  40. 40.
    Che, M., Grellmann, W., Seidler, S.: Appl. Polym. Sci. 64, 1079–1090 (1997)CrossRefGoogle Scholar
  41. 41.
    Ross, D.W.: Lysosomes and storage diseases. MA Thesis. Columbia University, New York (1977)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Pawel Popielski
    • 1
    Email author
  • Zygmunt Wrobel
    • 1
  • Robert Koprowski
    • 1
  1. 1.Institute of Computer ScienceUniversity of SilesiaSosnowiecPoland

Personalised recommendations