Apply GPCA to Motion Segmentation
Hongchuan Yu, Jian Jun Zhang
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DOI: 10.4236/jilsa.2011.31006   PDF    HTML     5,035 Downloads   9,302 Views  

Abstract

In this paper, we present a motion segmentation approach based on the subspace segmentation technique, the genera-lized PCA. By incorporating the cues from the neighborhood of intensity edges of images, motion segmentation is solved under an algebra framework. Our main contribution is to propose a post-processing procedure, which can detect the boundaries of motion layers and further determine the layer ordering. Test results on real imagery have confirmed the validity of our method.

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H. Yu and J. Zhang, "Apply GPCA to Motion Segmentation," Journal of Intelligent Learning Systems and Applications, Vol. 3 No. 1, 2011, pp. 45-54. doi: 10.4236/jilsa.2011.31006.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] R. Vidal and Y. Ma, “A Unified Algebraic Approach to 2-D and 3-D Motion Segmentation,” Journal of Mathematical Imaging and Vision, Vol. 25, No. 3, 2006, pp. 403-421. doi:10.1007/s10851-006-8286-z
[2] R. Vidal, Y. Ma and S. Sastry, “Generalized Principal Component Analysis (GPCA),” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 12, 2005, pp. 1-15. doi:10.1109/TPAMI.2005.244
[3] J. Y. A. Wang and E. H. Adelson, “Layered Representation for Motion Analysis,” IEEE Conference on Computer Vision and Pattern Recognition, New York, 15-17 June 1993, pp. 361-366. doi:10.1109/CVPR.1993.341105
[4] R. Szeliski, S. Avidan and P. Anandan, “Layer Extraction from Multiple Images Con-taining Reflections and Transparency,” IEEE Conference on Computer Vision and Pattern Recognition, Hilton Head, 13-15 June 2000, pp. 246-253.
[5] M. J. Black and D. J. Fleet, “Probabilistic Detection and Tracking of Motion Boundaries,” International Journal of Computer Vision, Vol. 38, No. 3, 2000, pp. 231-245. doi:10.1023/A:1008195307933
[6] P. Smith, T. Drummond and R. Cipolla, “Layered Motion Segmentation and Depth Ordering by Tracking Edges,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No. 4, 2004, pp.479-494. doi:10.1109/ TPAMI.2004.1265863
[7] A. S. Ogale, C. Fermuller and Y. Aloimonos, “Motion Segmentation Using Occlusions,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 27, No. 6, 2005, pp. 988-992. doi:10.1109/TPAMI.2005.123
[8] D. J. Fleet, M. J. Black, Y. Yacoob and A. D. Jepson, “Design and Use of Linear Models for Image Motion Analysis,” International Journal Computer Vision, Vol. 36, No. 3, 2000, pp. 171-193. doi:10.1023/A:100815620 2475
[9] W. Yu, G. Sommer and K. Daniilidis, “Multiple Motion Analysis: In Spatial or in Spectral Domain,” Computer Vision and Image Understanding, Vol. 90, No. 2, 2003, pp. 129-152. doi:10.1016/S1077-3142(03)00011-0
[10] M. P. Kumar, P. H. S. Torr and A. Zisserma, “Learning Layered Motion Segmenta-tion of Video,” Proceedings of the 10th IEEE International Conference on Computer Vision, Beijing, 17-20 October 2005, pp. 33-40. doi:10.110 9/ICCV.2005.138
[11] M. Irani, P. Anandan, J. Bergen, R. Kumar and S. Hsu, “Efficient Repre-sentations of Video Sequences and Their Representations,” Signal Processing: Image Communication, Vol. 8, No. 4, 1996, pp. 327-351. doi:10.1016/0923- 5965(95)00055-0
[12] M. Irani, B. Rousso and S. Peleg, “Computing Occluding and Transparent Motions,” International Journal of Computer Vision, Vol. 2, No. 1, 1994, pp. 5-16. doi:10. 1007/BF01420982
[13] G. Csurka and P. Bouthemy, “Direct Identification of Moving Objects and Background from 2D Motion Models,” Proceedings of IEEE International Conference of Computer Vision, Kerkyra, 20-27 September 1999, pp. 566-571. doi:10.1109/ICCV.1999.791274
[14] T. Papadimitriou and K. I. Diamantaras, et al., “Video Scene Segmentation Using Spatial Contours and 3D Robust Motion Estimation,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 14, No. 4, 2004, pp. 485-497. doi:10.1109/TCSVT.2004.825562
[15] A. S. Ogale and Y. Aloimonos, “A Roadmap to the Integration of Early Visual Modules,” International Journal of Computer Vision, Vol. 72, No. 1, 2007, pp. 9-25. doi: 10.1007/s11263-006-8890-9
[16] Generalized Principal Components Analysis matlab codes available at http://perception.csl.uiuc.edu/gpca/
[17] Video sequences available at http://www.cipr.rpi.edu/ resource/sequences/
[18] R. Vidal, “Generalized Principal Component Analysis (GPCA): An Algebraic Geometric Approach to Subspace Clustering and Motion Segmentation,” Ph.D. Thesis, Electrical Engineering and Computer Sciences, University of California at Berkeley, 2003.

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