Fast computational methods for visually guided robots

Proceedings - IEEE International Conference on Robotics and Automation

Volumn 2005, Issue , 2005, Pages 138-143

  Mahdaviani, Maryam ^a   De Freitas, Nando ^a   Fraser, Bob ^a   Hamze, Firas ^a  

^a UNIVERSITY OF BRITISH COLUMBIA   (Canada)

Author keywords

Fast gauss transform; Interactive robots; Krylov subspace methods; Learning; Visually guided mobile robots

Indexed keywords

FAST GAUSS TRANSFORMS; INTERACTIVE ROBOTS; KRYLOV SUBSPACE METHODS; VISUALLY GUIDED MOBILE ROBOTS;

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTATIONAL METHODS; COSTS; OBJECT RECOGNITION; ROBOTICS;

MOBILE ROBOTS;

EID: 33846159526     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ROBOT.2005.1570109     Document Type: Conference Paper

References (18)

1. B J C Baxter and G Roussos, A new error estimate of the fast Gauss transform, SIAM Journal of Scientific Computing 24 (2002), no. 1, 257-259.
2. R K Beatson, J B Cherrie, and C T Mouat, Fast fi tting of radial basis functions: Methods based on preconditioned GMRES iteration, Advances in Computational Mathematics 11 (1999), 253-270.
3. R K Beatson and L Greengard, A short course on fast multipole methods, Multilevel Methods and Elliptic PDEs (M Ainsworth, J Levesley, W Light, and M Marletta, eds.), Oxford University Press, 1997, pp. 1-37.
4. M Belkin and P Niyogi, Laplacian eigenmaps for dimensionality reduction and data representation, Neural Computation 15 (2003), no. 6, 1373-1396.
5. J W Demmel, Applied numerical linear algebra, SIAM, 1997.
6. G H Golub and C F Van Loan, Matrix computations, third ed., Johns Hopkins University Press, Baltimore, 1996.
7. A G Gray and A W Moore, 'N-Body' problems in statistical learning, NIPS, 2000, pp. 521-527.
8. A G Gray and A W Moore, Rapid evaluation of multiple density models, Artificial Intelligence and Statistics, 2003.
9. L Greengard and V Rokhlin, A fast algorithm for particle simulations, Journal of Computational Physics 73 (1987), 325-348.
10. L Greengard and J Strain, The fast Gauss transform, SIAM Journal of Scientific Statistical Computing 12 (1991), no. 1, 79-94.
11. L Greengard and X Sun, A new version of the Fast gauss transform, Documenta Mathematica ICM (1998), no. 3, 575-584.
12. D G Lowe, Object recognition from local scale-invariant features, ICCV, 1999.
13. A W Moore, The Anchors Hierarchy: Using the triangle inequality to survive high dimensional data, Uncertainty in Artifi cial Intelligence, AAAI Press, 2000, pp. 397-405.
14. J Shi and J Malik, Normalized cuts and image segmentation, IEEE Conference on Computer Vision and Pattern Recognition, 1997, pp. 731-737.
15. C Yang, R Duraiswami, N A Gumerov, and L S Davis, Improved fast Gauss transform and effi cient kernel density estimation, International Conference on Computer Vision (Nice), 2003.
16. D Zhou, J Weston, A Gretton, O Bousquet, and B Scholkopf, Ranking on data manifolds, NIPS, 2004.
17. X Zhu, J Lafferty, and Z Ghahramani, Combining active learning and semi-supervised learning using Gaussian fi elds and harmonic functions, ICML, 2003, pp. 58-65.
18. _, Semi-supervised learning using Gaussian fi elds and harmonic functions, ICML, 2003, pp. 912-919.