Abstract.

The paper considers the problem of estimating a transformation connecting two images of one plane object. The method is proposed for calculating the parameters of projective transformation by data consisting of points and lines. The results of the experiments on synthetic data are presented, in which the rate of the algorithm convergence was studied depending on the ratio of primitives in the original dataset. Also the advantage of using directly straight lines, rather than points of their intersection is experimentally shown.

Keywords:

projective transformation, RANSAC.

PP. 79-91.

REFERENCES

1. Lowe D. G. Distinctive Image Features from Scale-Invariant Keypoints // International Journal of Computer Vision archive, 2004. Vol. 60, Issue 2, pp. 91-110.
2. Bay H., Ess A., Tuytelaars T., Gool L. V. Speeded-Up Robust Features (SURF) // Computer Vision and Image Understanding archive, 2008. Vol. 110, Issue 3, pp. 346-359.
3. Lepetit, V., Fua, P. Keypoint recognition using randomized trees // IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006. 28, pp. 1465‐1479
4. Fan B., Kong Q., Trzcinski T. Receptive Fields Selection for Binary Feature Description // IEEE Transaction on Image Processing, 2014. 23(6), pp. 2583-2595.
5. Strang G. Lineynaya algebra i ee primeneniya // M.: Mir, 1980. 454 p.
6. Theil H. A rank-invariant method of linear and polynomial regression analysis // Proceedings of the Royal Netherlands Academy of Sciences, 1950. 53, pp. 386–392, 521–525, 1397–1412.
7. Sen P. K., Kumar P. Estimates of the regression coefficient based on Kendall's tau, Journal of the American Statistical Association, 1968. 63, pp. 1379–1389.
8. Siegel A. F. Robust Regression Using Repeated Medians // Biometrika, 1982. Vol. 69, No. 1 pp. 242-244.
9. Rousseeuw P. J. Least median of squares regression // Journal of the American Statistical Association, 1984. Vol. 79, No. 388, pp. 871–880.
10. 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, 1981. Vol. 24, pp. 381–395.
11. Chum O., Matas J. Matching with PROSAC - progressive sample consensus // IEEE Conference on Computer Vision and
Pattern Recognition, 2005. pp. 220-226
12. Torr P.H.S., Zisserman A. A MLESAC: A New Robust Estimator with Application to Estimating Image Geometry // Computer
Vision and Image Understanding, 2000. Vol. 78, pp. 138--156
13. Hough P.V.C. Machine Analysis of Bubble Chamber Pictures // Proceeding of International Conference on High Energy Accelerators
and Instrumentation, 1959. pp. 554-558
14. Nikolaev D.P., Karpenko S.M., Nikolaev I.P., Nikolayev P.P. Hough transform: underestimated tool in the computer vision
field // Proceedings of the 22th European Conference on Modelling and Simulation, 2008. pp. 238-246
15. Grompone von Gioi R., Jakubowicz J., Morel J.M., Randall G. LSD: a fast line segment detector with a false detection control
// IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010. 32 (4), pp. 722–732
16. Akinlar C., Topal C. EDLines: A real-time line segment detector with a false detection control // Pattern Recognition Letters,
2011. 32(13), pp. 1633-1642
17. Hartley R., Zisserman A. Multiple View Geometry in Computer Vision // New York: Cambridge University Press, 2004. 655 pp.
18. Pevzner S. Proektivnaya geometriya // M: "Prosveshchenie", 1980. 128 p.
19. Ponarin Y. Affinnaya i proektivnaya geometriya // MCNMO, 2009. 288 p.
20. Shemiakina J., Zhukovsky A., Faradjev I. Issledovanie algoritmov vychisleniya proektivnogo preobrazovaniya v zadache
navedeniya na planarnyy obekt po osobym tochkam // Iskusstvennyy intellekt i prinyatie resheniy, 2017. 1, pp. 43-49
21. Bhattacharya P., Rosenfeld A., Weiss I. Point-to-line mappings as Hough transforms // Pattern Recognition Letters, 2002.
Vol. 23, pp. 1705-1710.