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Virtual Journal for Biomedical Optics

Virtual Journal for Biomedical Optics


  • Editors: Andrew Dunn and Anthony Durkin
  • Vol. 8, Iss. 8 — Sep. 4, 2013

Supervised preserving projection for learning scene information based on time-of-flight imaging sensor

Yi Jiang, Yong Liu, Yunqi Lei, and Qicong Wang  »View Author Affiliations

Applied Optics, Vol. 52, Issue 21, pp. 5279-5288 (2013)

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In this paper, we propose a new supervised manifold learning approach, supervised preserving projection (SPP), for the depth images of a 3D imaging sensor based on the time-of-flight (TOF) principle. We present a novel manifold sense to learn scene information produced by the TOF camera along with depth images. First, we use a local surface patch to approximate the underlying manifold structures represented by the scene information. The fundamental idea is that, because TOF data have nonstatic noise and distance ambiguity problems, the surface patches can more efficiently approximate the local neighborhood structures of the underlying manifold than TOF data points, and they are robust to the nonstatic noise of TOF data. Second, we propose SPP to preserve the pairwise similarity between the local neighboring patches in TOF depth images. Moreover, SPP accomplishes the low-dimensional embedding by adding the scene region class label information accompanying the training samples and obtains the predictive mapping by incorporating the local geometrical properties of the dataset. The proposed approach has advantages of both classical linear and nonlinear manifold learning, and real-time estimation results of the test samples are obtained by the low-dimensional embedding and the predictive mapping. Experiments show that our approach obtains information effectively from three scenes and is robust to the nonstatic noise of 3D imaging sensor data.

© 2013 Optical Society of America

OCIS Codes
(150.6910) Machine vision : Three-dimensional sensing
(330.5000) Vision, color, and visual optics : Vision - patterns and recognition

ToC Category:
Machine Vision

Original Manuscript: February 22, 2013
Revised Manuscript: June 5, 2013
Manuscript Accepted: June 24, 2013
Published: July 19, 2013

Virtual Issues
Vol. 8, Iss. 8 Virtual Journal for Biomedical Optics

Yi Jiang, Yong Liu, Yunqi Lei, and Qicong Wang, "Supervised preserving projection for learning scene information based on time-of-flight imaging sensor," Appl. Opt. 52, 5279-5288 (2013)

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  1. A. Kolb, E. Barth, R. Koch, and R. Larsen, “Time-of-flight sensors in computer graphics,” in Eurographics 2009 (2009), pp. 119–134.
  2. V. Ganapathi, C. Plagemann, D. Koller, and S. Thrun, “Real time motion capture using a single time-of-flight camera,” in IEEE Conference on Computer Vision and Pattern Recognition (2010), pp. 755–762.
  3. L. Zhu, J. Zhou, J. Song, Z. Yan, and Q. Gu, “A practical algorithm for learning scene information from monocular video,” Opt. Express 16, 1448–1459 (2008). [CrossRef]
  4. J. B. Tenenbaum, V. de Silva, and J. C. Langford, “A global geometric framework for nonlinear dimensionality reduction,” Science 290, 2319–2323 (2000). [CrossRef]
  5. M. Belkin and P. Niyogi, “Laplacian eigenmaps for dimensionality reduction and data representation,” Neural Comput. 15, 1373–1396 (2003). [CrossRef]
  6. S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science 290, 2323–2326 (2000). [CrossRef]
  7. X. He and P. Niyogi, “Locality preserving projections,” Adv. Neural Inf. Process. Syst. 16, 100–200 (2004).
  8. X. He, D. Cai, S. Yan, and H. Zhang, “Neighborhood preserving embedding,” in Tenth IEEE International Conference on Computer Vision (2005), Vol. 2, pp. 1208–1213.
  9. C. BenAbdelkader, “Robust head pose estimation using supervised manifold learning,” in Computer Vision—ECCV 2010, Vol. 6136 of Lecture Notes in Computer Science (Springer, 2010), pp. 518–531.
  10. Z. Zhang and H. Zha, “Principal manifolds and nonlinear dimension reduction via local tangent space alignment,” SIAM J. Sci. Comput. 26, 313–338 (2004). [CrossRef]
  11. T. Lin and H. Zha, “Riemannian manifold learning,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 796–809 (2008). [CrossRef]
  12. L. Jovanov, A. Pižurica, and W. Philips, “Fuzzy logic-based approach to wavelet denoising of 3D images produced by time-of-flight cameras,” Opt. Express 18, 22651–22676 (2010). [CrossRef]
  13. M. Belkin, P. Niyogi, and V. Sindhwani, “Manifold regularization: a geometric framework for learning from labeled and unlabeled examples,” J. Mach. Learn. Res. 7, 2399–2434 (2006).
  14. M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” J. Electron. Imaging 13, 146–168 (2004). [CrossRef]
  15. V. N. Balasubramanian, J. Ye, and S. Panchanathan, “Biased manifold embedding: a framework for person-independent head pose estimation,” in IEEE Conference on Computer Vision and Pattern Recognition, 2007 (2007), pp. 1–7.
  16. S. Yan, D. Xu, B. Zhang, and H. Zhang, “Graph embedding and extensions: a general framework for dimensionality reduction,” IEEE Trans. Pattern Anal. Mach. Intell. 29, 40–51 (2007). [CrossRef]
  17. X. He, M. Ji, and H. Bao, “Graph embedding with constraints,” in IJCAI’09 Proceedings of the 21st International Joint Conference on Artificial Intelligence (2009), pp. 1065–1070.

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