Browse > Article

Extraction of Corresponding Points Using EMSAC Algorithm  

Ye, Soo-Young (Pusan National University School of Medicine)
Jeon, Ah-Young (Dept. of Interdisciplinary program in Biomedical Engineering, Pusan National University)
Jeon, Gye-Rok (Dept. of Interdisciplinary program in Biomedical Engineering, Pusan National University)
Nam, Ki-Gon (Dept. of Electronics Engr., Pusan National University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea SP / v.44, no.4, 2007 , pp. 44-50 More about this Journal
Abstract
In this paper, we proposed the algorithm for the extraction of the corresponding points from images. The proposed algorithm EMSAC is based on RANSAC and EM algorithms. In the RANSAC procedure, the N corresponding points are randomly selected from the observed total corresponding points to estimate the homography matrix, H. This procedure continues on its repetition until the optimum H are estimated within number of repetition maximum. Therefore, it takes much time and does not converge sometimes. To overcome the drawbacks, the EM algorithm was used for the selection of N corresponding points. The EM algorithm extracts the corresponding points with the highest probability density to estimate the optimum H. By the experiments, it is demonstrated that the proposed method has exact and fast performance on extraction of corresponding points by combining RANSAC with EM.
Keywords
Correspondences; Expectation Maximization; MLESAC; RANSAC;
Citations & Related Records
연도 인용수 순위
  • Reference
1 M. A. Fischler and R. C. Bolles, 'Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography,' Comm. of the ACM, vol. 24, no. 6, pp. 381-395, Jun. 1981   DOI   ScienceOn
2 P. H. S Torr and D.W Murray, 'Statistical detection of independent movement from a moving camera,' Image and Vision Computing, vol. 11, no. 4, pp. 180-187, 1993   DOI   ScienceOn
3 D. W. Paglieroni, 'Distance Transforms: Properties and Machine Vision Applications,' CVGIP: Graphical Models and Image Processing, vol. 54, no. 1, pp. 56-74, Jan. 1992   DOI
4 G. Borgefors, 'Distance Transformations in Digital Images,' Computer Vision, Graphics, and Image Processing, vol. 34, pp. 344-371, 1986   DOI
5 Perez, M. M.; Dennis, T. J., 'An adaptive implementation of the SUSAN method for image edge and feature detection,' International Conference on Image Processing, vol. 2, pp. 394 - 397, Oct. 1997
6 Yuille, A., Ferraro, M., Zhang, T., 'Surface shape from warping,' IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 846 - 851, June 1997
7 DeCarlo, D., Metaxas, D., 'Adaptive shape evolution using blending,' Fifth International Conference on Computer Vision, pp. 834 - 839, June 1995
8 E. Malis and R. Cipolla, 'Multi-view constraints between collineations: application to selfcalibration from unknown planar structures,' In European Conference on Computer Vision, June 2000
9 G. Xu and Z. Zhang, Epipolar geometry in stereo, motion and object recognition, Kluwer Academic Pulishers, 1996
10 H. Akaike. 'Information theory and an extension of the maximum likelihood principle,' 2nd International Symposium on Information Theory, Armenia, 1971