A robust subspace algorithm for projective reconstruction from multiple images

被引：0
|
作者：
机构：
[1] School of Management Science and Engineering, Shandong University of Finance and Economics
[2] School of Computer Science and Technology, Shandong University
[3] CVIC Software Engineering Co., Ltd.
来源：
Guo, J.-D. (gjd730210@163.com) | 1600年 / Science Press卷 / 36期
关键词：
Augmented Lagrange multiplier (ALM); Factorization method; Missing data; Outlier; Subspace method;
D O I：
10.3724/SP.J.1016.2013.02560
中图分类号：
学科分类号：
摘要：
All the points are visible in all views and mismatched datum (outliers) are not presented in measure matrix are necessary conditions of the existing subspace method. To eliminate the harmful effect of outliers and missing datum to factorization method, a robust subspace projective reconstruction method is proposed in this paper. Augmented Lagrange multipliers (ALM) imposed rank constraints can be used for solving a convex optimization problem. By minimizing a combination of nuclear norm and L1-norm, low-dimension subspaces of measure matrix can be obtained in this convex optimization. Projective shape and projective depths are alternatively estimated in the subspace projective reconstruction method. The two sub-problems are formulated in a subspace framework and same objective function is iteratively minimized on two independent variable sets. The above improvements can effectively ensure the convergence of the iterative process. Comparing with Tang's subspace method, experimental results are provided to illustrate the validity and reliability of the proposed algorithm in this paper.
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页码：2560 / 2576
页数：16
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共 20 条
  • [1] Hartley R.I., Zisserman A., Euclidean reconstruction from uncalibrated views, Proceedings of the 2nd Joint European-US Workshop on Applications of Invariance in Computer Vision, pp. 235-256, (1994)
  • [2] Faugeras O.D., Stratification of three-dimensional vision: Projective, affine, and metric representations, Journal of the Optical Society of America-A, 12, 3, pp. 465-484, (1995)
  • [3] Sturm P., Triggs B., A factorization based algorithm for multi-image projective structure and motion, Proceedings of the 4th European Conference on Computer Vision, pp. 709-720, (1996)
  • [4] Triggs B., Factorization methods for projective structure and motion, Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 845-851, (1996)
  • [5] Mahamud S., Hebert M., Omori Y., Ponce J., Provably-convergent iterative methods for projective structure from motion, Proceedings of the 2001 IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1018-1025, (2001)
  • [6] Mahamud S., Hebert M., Iterative projective reconstruction from multiple views, Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition, pp. 430-437, (2000)
  • [7] Tang W.K., Hung Y.S., A factorization-based method for projective reconstruction with minimization of 2D reprojection errors, Proceedings of the 24th Annual Pattern Recognition Symposium DAGM 2002, pp. 387-394, (2002)
  • [8] Tang W.K., Hung Y.S., A column-space approach to projective reconstruction, Computer Vision and Image Understanding, 101, 3, pp. 166-176, (2006)
  • [9] Heyden A., Berthilsson R., Sparr G., An iterative factorization method for projective structure and motion from image sequences, Image and Vision Computing, 17, 13, pp. 981-991, (1999)
  • [10] Sparr G., Simultaneous reconstruction of scene structure and camera locations from uncalibrated image sequences, Proceedings of the 13th International Conference on Pattern Recognition, pp. 328-333, (1996)
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