Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Preliminary coarse image registration by using straight lines found on them for constructing super resolution mosaics and 3D scene recovery

  • Published:
Programming and Computer Software Aims and scope Submit manuscript

Abstract

An algorithm of coarse image registration of a 3D scene taken from different camera perspectives is proposed. The algorithm uses information on geometrical parameters of straight lines found on the images and on distribution of color and/or brightness around these lines. Colors are taken into account by using the fuzzy logic technique. The result of the algorithm operation is a planar projective transformation (planar homography) matching approximately the images. In order to use the technique in algorithms of 3D scene reconstruction, an estimate of size of the window used for searching correspondent points after the coarse image registration is obtained.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Hartley, R. and Zisserman, A., Multiple View Geometry in Computer Vision, Cambridge University Press, 2004.

  2. Capel, D. and Zisserman, A., Computer Vision Applied to Super Resolution, IEEE Signal Processing Magazine, 2003, pp. 75–86, http://www.robots.ox.ac.uk/%7evgg/publications/html/index.html

  3. Reddy S.B. and Chatterji, B.N., An FFT-Based Technique for Translation, Rotation, and Scale-Invariant Image Registration, IEEE PAMI, 1996, vol. 5, no. 8, pp. 1266–1271.

    Google Scholar 

  4. Shi, J. and Tomasi, C., Good Features to Track, IEEE Conf. on Comput. Vision and Pattern Recognition (CVPR’94), Seattle, 1994.

  5. Dufournaud, Y., Schmid, C., and Horaud, R., Matching Images with Different Resolutions, Proc. of IEEE Conf. on Comput. Vision and Pattern Recognition (CVPR’00), Hilton Head Island, SC, USA, 2000, vol. 1, pp. 612–618, http://citeseer.ist.psu.edu/dufournaud00matching.html.

    Google Scholar 

  6. Birchfield, S. and Tomasi, C., Multiway Cut for Stereo and Motion with Slanted Surfaces, Proc. of the Seventh IEEE Int. Conf. on Comput. Vision, Kerkyra, Greece, 1999, pp. 489–495, http://citeseer.ist.psu.edu/birchfield 99multiway.html.

  7. Harris, C.G. and Stephens, M., A Combined Corner and Edge Detector, Proc. of the 4th Alvey Vision Conf., Manchester, 1988, pp. 147–151.

  8. Lindeberg, T., Scale-Space Theory in Computer Vision, Dordrecht, The Netherlands: Kluwer, 1994, also http://www.nada.kth.se/%7Etony/earlyvision.html.

    Google Scholar 

  9. Schmid, C. and Mohr, R., Local Grayvalue Invariants for Image Retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 1997, vol. 19, no. 5, pp. 530–534, http://citeseer.ist.psu.edu/schmid97local.html.

    Article  Google Scholar 

  10. Florack, L.M.J., Ter Haar Romeny, B.M., Koenderink, J.J., and Viergever, M.A., General Intensity Transformations and Differential Invariants, J. Mathematical Imaging Vision, 1994, vol. 4, no. 2, pp. 171–187, http://www.bmi2.bmt.tue.nl/image-analysis/People/LFlorack/Extensions/Flor94d.pdf.

    Article  MathSciNet  Google Scholar 

  11. Fischler, M. and Bolles, R., Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography, Commun. ACM, 1981, vol. 24, no. 6, pp. 381–395.

    Article  MathSciNet  Google Scholar 

  12. Davis, J., Mosaics of Scenes with Moving Objects, Proc. Comput. Vision and Pattern Recognition Conf., 1998, pp. 354–360, http://citeseer.ist.psu.edu/danis98mosaics.html.

  13. Kolmogorov, V. and Zabih, R., Computing Visual Correspondence with Occlusions Using Graph Cuts, Int. Conf. on Comput. Vision, Vancouver, 2001, http://www.cs.cornell.edu/rdz/Papers/KZ-ICCV01-tr.pdf.

  14. Lin, M.H. and Tomasi, C., Surfaces with Occlusions from Layered Stereo, IEEE Comput. Soc. Conf. on Comput. Vision and Pattern Recognition, 2003.

  15. Scharstein, D. and Szeliski, R., A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms, IJCV, 2002, vol. 47, no. 1/2/3, pp. 7–42, http://cat.middlebury.edu/stereo.

    Article  MATH  Google Scholar 

  16. Poelman, C.J. and Kanade, T., A Paraperspective Factorization Method for Shape and Motion Recovery, IEEE Trans. on Pattern Analysis and Machine Intelligence, 1997, vol. 19, no. 3, pp. 206–218, http://citeseer.ist.psu.edu/poelman92paraperspective.html.

    Article  Google Scholar 

  17. Salgado, J.P. and Costeira, A., A Multi-Body Factorization Method for Motion Analysis. Tese para Obtencao do Grau de Doutor em Engenharia Electrotecnica e de Computadores, Universitade Technica de Lisboa Instituto Superior Rechnico, Lisboa, 1995, http://omni.isr.ist.utl.pt/~jpc/pubs.html.

    Google Scholar 

  18. Sturm, P. and Triggs, B., A Factorization Based Algorithm for Multi-Image Projective Structure and Motion, The 4th European Conf. on Comput. Vision, Cambridge, England, 1996, pp. 709–720.

  19. Sveshnikova, N.V. and Yurin, D.V., A Priori and A Posteriori Error Estimates in Recovery of 3D Scenes by Factorization Algorithms, Programmirovanie, 2004, no. 5, pp. 48–68 [Programming Comput. Software (Engl. Transl.), 2004, vol. 30, no. 5, pp. 278–295].

  20. Sveshnikova, N.V. and Yurin, D.V., The Factorization Algorithms: Results Reliability and Application for the Epipolar Geometry Recovery, Proc. of the 16th Int. Conf. on Comput. Graphics and Application Graphi-Con′2006, 2006, Novosibirsk Akademgorodok, Russia.

  21. Pritchett, P. and Zisserman, A., Wide Baseline Stereo Matching, Proc. of the 6th Int. Conf. on Comput. Vision, Bombay, 1998, pp. 754–760, http://www.robots.ox.ac.uk/%7evgg/publications/html/index.html.

  22. Cormen, T.H., Leiserson, C.E., Rivest, R.L, and Stein, C., Introduction to Algorithms, The MIT Press, 2001, 2nd ed.

  23. Hartley, R.I., Theory and Practice of Projective Rectification, Int. J. Comput. Vision, 1999, vol. 35, no. 2, pp. 115–127.

    Article  MathSciNet  Google Scholar 

  24. Zhang, Z., Determining the Epipolar Geometry and its Uncertainty: A Review, Tech. Report 2927, INRIA, Sophia-Antipolis, France, 1996, http://citeseer.ist.psu.edu/zhang98determining.html.

    Google Scholar 

  25. Armangué, X., Pegès, J., and Salvi, J., Comparative Survey on Fundamental Matrix Estimation, http://citeseer.ist.psu.edu/483847.html.

  26. Baumberg, A., Reliable Feature Matching across Widely Separated Views, Proc. CVPR, 2000, pp. 774–781, http://citeseer.ist.psu.edu/baumberg00reliable.html.

  27. Mikolajczyk, K. and Schmid, C., Scale and Affine Invariant Interest Point Detectors, Int. J. Comput. Vision, 2004, vol. 60, no. 1, pp. 63–86, http://www.robots.ox.ac.uk/~vgg/research/affine/det_eval_files/mikolajczyk_ijvc2004.pdf.

    Article  Google Scholar 

  28. Lowe, D.G., Distinctive Image Features from Scale-Invariant Keypoints, Int. J. Comput. Vision, 2004, vol. 69, no. 2, pp. 91–110, http://www.cs.ubc.ca/spider/lowe/pubs.html.

    Article  Google Scholar 

  29. Gouet, V., Montesinos, P., and Pele, D., A Fast Matching Method for Color Uncalibrated Images using Differential Invariants, Proc. of the British Machine Vision Conf., Southampton, 1998, http://citeseer.ist.psu.edu/article/gouet98fast.html.

  30. Csurka, G., Zeller, C., Zhang, Z., and Faugeras, O.D., Characterizing the Uncertainty of the Fundamental Matrix, Comput. Vision and Vision Understanding (CVIU), 1997, vol. 68, no. 1, pp. 18–36, http://citeseer.ist.psu.edu/csurka95characterizing.html.

    Article  Google Scholar 

  31. Matas, J., Chum, O., Martin, U., and Pajdla, T., Robust Wide Baseline Stereo from Maximally Stable Extremal Regions, Proc. of the British Machine Vision Conf., London, 2002, vol. 1, pp. 384–393, http://cmp.felk.cvut.cz/~matas.

    Google Scholar 

  32. Princen, J.P., Illingworth, J., and Kittler, J.V., A Formal Definition of the Hough Transform: Properties and Relationships, J. Mathematical Imaging Vision, 1992, vol. 1, no. 1, pp. 153–168.

    Article  Google Scholar 

  33. Kiryat, N., Eldar, Y., and Bruckstein, A.M., A Probabilistic Hough Transform, Pattern Recognition, 1991, vol. 24, pp. 303–316.

    Article  MathSciNet  Google Scholar 

  34. Xu. L., Oja, E., and Kultanen, P., A New Curve Detection Method: Randomized Hough Transform (RHT), Pattern Recognition Letters, 1990, vol. 11, no. 5, pp. 331–338.

    Article  MATH  Google Scholar 

  35. Kalvianen, H. and Hirvonen, P., Connective Randomized Hough Transform (CRHT), Proc. of the 9th Scandinavian Conf. on Image Analysis, Uppsala, Sweden, 1995, vol. 2, pp. 1029–1036.

    Google Scholar 

  36. Toft, P.A., The Random Transform: Theory and Implementation, PhD Thesis, Technical University of Denmark, 1996.

  37. Cheyne, Gaw Ho, Young, R.C.D., Bradfield, C.D., and Chatwin, C.R., A Fast Hough Transform for the Parameterization of Straight Lines Using Fourier Methods, Real-Time Imaging, 2000, vol. 6, no. 2, pp. 113–127.

    Article  Google Scholar 

  38. Volegov, D.B., Gusev, V.V., and Yurin, D.V., Straight Line Detection on Images via Hartley Transform. Fast Hough Transform, Proc. of the 16-th Int. Conf. on Comput. Graphics and Application GraphiCon’2006, 2006, Novosibirsk, Akademgorodok, Russia.

  39. Donoho, D.L. and Huo, X., Beamlets and Multiscale Image Analysis, http://www.stat.stanford.edu/donoho/Reports/.http://citeseer.ist.psu.edu/donoho01beamlets.html.

  40. Donoho, D. and Huo, X., Applications of Beamlets to Detection and Extraction of Lines, Curves and Objects in Very Noisy Images, http://citeseer.ist.psu.edu/446366.html.

  41. Bracewell, R.N., The Hartley Transform, Oxford University Press, 1986.

  42. Press, et al., Numerical Recipes in C, Cambridge University Press, 1992.

  43. Theußl, T., Tobler, R.F., and Gröller, E., The Multi-Dimensional Hartley Transform as a Basis for Volume Rendering, WSCG 2000 Conf. Proc., http://citeseer.ist.psu.edu/450842.html, http://wscg.zcu.cz/wscg2000/Papers_2000/W11.pdf.gz.

  44. Zhang, Z., A Flexible New Technique for Camera Calibration, IEEE Trans. on Pattern Analysis and Machine Intelligence, 2000, vol. 22, no. 11, pp. 1330–1334.

    Article  Google Scholar 

  45. Di Zenzo, S., A Note on the Gradient of Multi-image, Comput. Vision Graphics Image Process, 1986, vol. 33, pp. 116–125.

    Article  Google Scholar 

  46. Klir, G. and Bo Yuan, Fuzzy Sets and Fuzzy Logic, 1995.

  47. Zimmermann, H., Fuzzy Set Theory and Its Applications, 2001.

Download references

Author information

Authors and Affiliations

  1. Department of General and Applied Physics, Moscow Institute of Physics and Technology, Institutskii proezd 9, Dolgoprudnyi, Moscow oblast, 141700, Russia

    D. B. Volegov

  2. Department of Computational Mathematics and Cybernetics, Moscow State University, Moscow, 119992, Russia

    D. V. Yurin

Authors
  1. D. B. Volegov
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. D. V. Yurin
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to D. B. Volegov.

Additional information

Original Russian Text © D.B. Volegov, D.V. Yurin, 2008, published in Programmirovanie, 2008, Vol. 34, No. 5.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Volegov, D.B., Yurin, D.V. Preliminary coarse image registration by using straight lines found on them for constructing super resolution mosaics and 3D scene recovery. Program Comput Soft 34, 279–293 (2008). https://doi.org/10.1134/S0361768808050058

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0361768808050058

Keywords

Search

Navigation