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Correction of Missing Feature Points for 3D Modeling from 2D object images
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 Title & Authors
Correction of Missing Feature Points for 3D Modeling from 2D object images
Koh, Sung-shik;
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 Abstract
How to recover from the multiple 2D images into 3D object has been widely studied in the field of computer vision. In order to improve the accuracy of the recovered 3D shape, it is more important that noise must be minimized and the number of image frames must be guaranteed. However, potential noise is implied when tracking feature points. And the number of image frames which is consisted of an observation matrix usually decrease because of tracking failure, occlusions, or low image resolution, and so on. Therefore, it is obviously essential that the number of image frames must be secured by recovering the missing feature points under noise. Thus, we propose the analytic approach which can control directly the error distance and orientation of missing feature point by the geometrical properties under noise distribution. The superiority of proposed method is demonstrated through experimental results for synthetic and real object.
 Keywords
missing feature points;geometrical approach;SVD factorization;3D reconstruction;
 Language
Korean
 Cited by
 References
1.
M. Irani, P. Anandan, "Factorization with Uncertainty," in Proceeding of European Conference on Computer Vision, Dublin, Ireland, pp. 539-553, 2000.

2.
R. Szeliski and S. B. Kang, "Shape Ambiguities in Structure-from-Motion," IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol.19. pp. 506-512, 1997. crossref(new window)

3.
Tomasi and Kanade "Shape and Motion from Image Streams under Orthography: A factorization method," International Journal of Computer Vision, Vol.9, No.2, pp. 137-154, 1992. crossref(new window)

4.
Jacobs, "Linear Fitting with Missing Data for Structurefrom- Motion," in Proceeding of Computer Vision and Image Understanding, Vol.82, pp. 57-81, 2001.

5.
F. C. Guerreiro and M. Q. Aguiar, "Estimation of Rank Deficient Matrices from Partial Observations: Two-Step Iterative Algorithms," in Proceeding of Energy Minimization Methods in Computer Vision and Pattern Recognition, pp. 450-466, 2003.

6.
H. Shum, K. Ikeuchi, and R. Reddy, "Principal Component Analysis with Missing Data and Its Applications to Polyhedral Object Modeling," IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol.17, No.9, pp. 854-867, 1995. crossref(new window)

7.
S. Soatto, and P. Perona, "Dynamic Rigid Motion Estimation from Weak Perspective," International Conference on Computer Vision, pp. 321-328, 1995.

8.
P. McLauchlan, I. Reid, D. Murray, "Recursive Affine Structure and Motion from Image Sequence," in Proceeding of European Conference on Computer Vision, pp. 217-224, 1994.

9.
R. Hartly, "Euclidean Reconstruction from Uncalibrated View," in Proceeding of 2nd European-US Workshop on Invariance, pp. 237-256, 1993.

10.
S. S. Koh, T.T. Zin, H. Hama, "Analysis of Geometrical Relations of 2D Affine-Projection Images and Its 3D Shape Reconstruction," The Institute of Electronics Engineers of Korea - Signal Processing, vol.44, no.4, pp.1-7, Jul. 2007.