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]

T. Lin and H. Zha, “Riemannian manifold learning,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 796–809 (2008).

[CrossRef]

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]

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).

M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” J. Electron. Imaging 13, 146–168 (2004).

[CrossRef]

X. He and P. Niyogi, “Locality preserving projections,” Adv. Neural Inf. Process. Syst. 16, 100–200 (2004).

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]

M. Belkin and P. Niyogi, “Laplacian eigenmaps for dimensionality reduction and data representation,” Neural Comput. 15, 1373–1396 (2003).

[CrossRef]

S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science 290, 2323–2326 (2000).

[CrossRef]

J. B. Tenenbaum, V. de Silva, and J. C. Langford, “A global geometric framework for nonlinear dimensionality reduction,” Science 290, 2319–2323 (2000).

[CrossRef]

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.

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.

A. Kolb, E. Barth, R. Koch, and R. Larsen, “Time-of-flight sensors in computer graphics,” in Eurographics 2009 (2009), pp. 119–134.

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).

M. Belkin and P. Niyogi, “Laplacian eigenmaps for dimensionality reduction and data representation,” Neural Comput. 15, 1373–1396 (2003).

[CrossRef]

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.

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.

J. B. Tenenbaum, V. de Silva, and J. C. Langford, “A global geometric framework for nonlinear dimensionality reduction,” Science 290, 2319–2323 (2000).

[CrossRef]

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.

X. He and P. Niyogi, “Locality preserving projections,” Adv. Neural Inf. Process. Syst. 16, 100–200 (2004).

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.

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.

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.

A. Kolb, E. Barth, R. Koch, and R. Larsen, “Time-of-flight sensors in computer graphics,” in Eurographics 2009 (2009), pp. 119–134.

A. Kolb, E. Barth, R. Koch, and R. Larsen, “Time-of-flight sensors in computer graphics,” in Eurographics 2009 (2009), pp. 119–134.

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.

J. B. Tenenbaum, V. de Silva, and J. C. Langford, “A global geometric framework for nonlinear dimensionality reduction,” Science 290, 2319–2323 (2000).

[CrossRef]

A. Kolb, E. Barth, R. Koch, and R. Larsen, “Time-of-flight sensors in computer graphics,” in Eurographics 2009 (2009), pp. 119–134.

T. Lin and H. Zha, “Riemannian manifold learning,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 796–809 (2008).

[CrossRef]

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).

X. He and P. Niyogi, “Locality preserving projections,” Adv. Neural Inf. Process. Syst. 16, 100–200 (2004).

M. Belkin and P. Niyogi, “Laplacian eigenmaps for dimensionality reduction and data representation,” Neural Comput. 15, 1373–1396 (2003).

[CrossRef]

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.

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.

S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science 290, 2323–2326 (2000).

[CrossRef]

M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” J. Electron. Imaging 13, 146–168 (2004).

[CrossRef]

S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science 290, 2323–2326 (2000).

[CrossRef]

M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” J. Electron. Imaging 13, 146–168 (2004).

[CrossRef]

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).

J. B. Tenenbaum, V. de Silva, and J. C. Langford, “A global geometric framework for nonlinear dimensionality reduction,” Science 290, 2319–2323 (2000).

[CrossRef]

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.

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]

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]

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.

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.

T. Lin and H. Zha, “Riemannian manifold learning,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 796–809 (2008).

[CrossRef]

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]

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]

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]

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.

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]

X. He and P. Niyogi, “Locality preserving projections,” Adv. Neural Inf. Process. Syst. 16, 100–200 (2004).

T. Lin and H. Zha, “Riemannian manifold learning,” IEEE Trans. Pattern Anal. Mach. Intell. 30, 796–809 (2008).

[CrossRef]

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]

M. Sezgin and B. Sankur, “Survey over image thresholding techniques and quantitative performance evaluation,” J. Electron. Imaging 13, 146–168 (2004).

[CrossRef]

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).

M. Belkin and P. Niyogi, “Laplacian eigenmaps for dimensionality reduction and data representation,” Neural Comput. 15, 1373–1396 (2003).

[CrossRef]

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]

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]

J. B. Tenenbaum, V. de Silva, and J. C. Langford, “A global geometric framework for nonlinear dimensionality reduction,” Science 290, 2319–2323 (2000).

[CrossRef]

S. T. Roweis and L. K. Saul, “Nonlinear dimensionality reduction by locally linear embedding,” Science 290, 2323–2326 (2000).

[CrossRef]

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]

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.

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.

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.

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.

A. Kolb, E. Barth, R. Koch, and R. Larsen, “Time-of-flight sensors in computer graphics,” in Eurographics 2009 (2009), pp. 119–134.

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.