Random Sampling and Voting Method for Three-Dimensional Reconstruction
In the series of papers, we proposed a method for threedimensional reconstruction from an image sequence without predetecting feature correspondences. In the method, we first collect all images and sample data, and second apply the reconstruction procedure. Therefore, the method is categorized into an off-line algorithm. In this paper, we deal with an on-line algorithm for three-dimensional reconstruction, if we sequentially measure images. Our method is based on the property that points and lines in space are uniquely computed from their projections between two images and among three images, respectively, if a camera system is calibrated. Using these property, our method determines both feature correspondences and three-dimensional positions of points and lines on an object.
Unable to display preview. Download preview PDF.
- 1.K. Kawamoto, A. Imiya: The detection of spatial points and lines by random sampling and voting procedure. (accepted for PRL). 193, 196Google Scholar
- 2.A. Imiya, K. Kawamoto: Performance analysis of shape recovery by random sampling and voting. Performance Characterization in Computer Vision, Kluwer Academic Publishers, Dordrecht, The Netherlands, (2000) 227–240. 193, 196Google Scholar
- 3.J. Illingworth, J. Kittler: A survey of the Hough transform. CVGIP, 44 (1988) 87–116. 193Google Scholar
- 5.H. Kälviäinen, P. Hirvonen, L. Xu, E. Oja: Probabilistic and non-probabilistic Hough transforms: overview and comparisons. IVC, 13 (1995) 239–252. 193Google Scholar
- 6.H. Kälviäinen: Motion detection by the RHT method: probability mechanisms and extensions. Research Report, 32, Lappeenranta University of Technology, Lappeenranta, Finland, (1992). 194Google Scholar
- 8.A. Imiya, K. Kawamoto: A dynamics of the Hough transform and artificial neural networks. Lecture Notes in Artificial Intelligence, Springer, Berlin, 1715 (1999) 36–50. 194Google Scholar
- 9.M. Bober, N. Georgis, J. Kittler: On accurate and robust estimation of fundamental matrix. CVIU, 72 (1998) 39–53. 194Google Scholar
- 10.P. H. S. Torr, A. Zisserman: MLESAC: a new robust estimator with application to estimating image geometry. CVIU, 78 (2000) 138–156. 194Google Scholar
- 11.M. A. Fischler, O. Firschein: Parallel guessing: a strategy for high-speed computation. PR, 20 (1987) 257–263. 194Google Scholar
- 12.S. Carlsson: Multiple image invariance using the double algebra. Lecture Notes in Computer Science, Springer, Berlin, 825 (1994) 145–164. 195Google Scholar
- 13.O. D. Faugeras, B. Mourrain: On the geometry and algebra of the point and line correspondences between N images. Technical Report N2665, INRIA, (1995). 195Google Scholar
- 15.A. Imiya: Detection of piecewise-linear signals by the randomized Hough transform. PRL, 17 (1996) 771–776. 197Google Scholar