Onboard contextual classification of 3-D point clouds with learned

Proceedings - IEEE International Conference on Robotics and Automation

Volumn , Issue , 2009, Pages 2009-2016

  Munoz, Daniel ^a   Vandapel, Nicolas ^a   Hebert, Martial ^a  

^a CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CONTEXTUAL CLASSIFICATION; CONTEXTUAL REASONING; EFFICIENT LEARNING; ENVIRONMENT MODELING; GRAPHICAL MODEL; HIGH-ORDER; HIGHER ORDER; LOCAL CLASSIFIER; MARKOV RANDOM FIELDS; MOBILE VEHICLE; POINT CLOUD; RANDOM FIELDS; STRUCTURED PREDICTION; SUB-GRADIENT OPTIMIZATION; TESTING TIME; VEHICLE SPEED;

GRAPHIC METHODS; HIDDEN MARKOV MODELS; ROBOTICS;

THREE DIMENSIONAL;

EID: 70350370245     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ROBOT.2009.5152856     Document Type: Conference Paper

References (22)

1. J.-F. Lalonde, N. Vandapel, and M. Hebert, "Data structures for efficient dynamic processing in 3-d, " The International Journal of Robotics Research, vol.26, no.8, pp. 777-796, August 2007.
2. D. Anguelov, B. Taskar, V. Chatalbashev, D. Koller, D. Gupta, G. Heitz, and A. Ng, "Discriminative learning of markov random fields for segmentation of 3-d scan data, " in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005.
3. D. Munoz, N. Vandapel, and M. Hebert, "Directional associative markov network for 3-d point cloud classification, " in Fourth International Symposium on 3D Data Processing, Visualization and Transmission, 2008.
4. P. Kohli, M. Kumar, and P. Torr, "P3 and beyond: Solving energies with higher order cliques, " in IEEE International Conference on Computer Vision and Pattern Recognition, 2007.
5. N. Ratliff, J. Bagnell, and M. Zinkevich, "Online subgradient methods for structured prediction, " in Eleventh International Conference on Artificial Intelligence and Statistics, 2007.
6. J. Lafferty, A. McCallum, and F. Pereira, "Conditional random fields: Probabilistic models for segmenting and labeling sequence data, " in International Conference on Machine Learning, 2001.
7. S. Li, Markov Random Field Modeling in Image Analysis. Springer- Verlag Telos, 2001.
8. V. Kolmogorov and R. Zabih, "What energy functions can be minimized via graph cuts?" PAMI, 2004.
9. Y. Boykov, O. Veksler, and R. Zabih, "Fast approximate energy minimization via graph cuts, " PAMI, 2001.
10. J. Yedidia, W. Freeman, and Y. Weiss, "Generalized belief propagation, " in NIPS, 2000.
11. V. Kolmogorov, "Convergent tree-reweighted message passing for energy minimization, " PAMI, 2006.
12. N. Komodakis, P. Torr, V. Kolmogorov, and Y. Boykov, "Discrete optimization methods in computer vision, " Tutorial at International Conference on Computer Vision (ICCV), October 14-15, 2007.
13. R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov, A. Agarwala, M. Tappena, and C. Rother, "A comparative study of energy minimization methods for markov random fields, " PAMI, 2007.
14. C. Sutton and A. McCallum, Introduction to Statistical Relational Learning, L. Getoor and B. Taskar, Eds. MIT press, 2006.
15. C. Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
16. B. Taskar, V. Chatalbashev, and D. Koller, "Learning associative markov networks, " in ICML, 2004.
17. A. Willis, J. Speicher, and D.Cooper, "Surface sculpting with stochastic deformable 3d surfaces, " in ICPR, 2004.
18. G. Medioni, M.-S. Lee, and C.-K. Tang, A Computational Framework for Segmentation and Grouping. Elsevier, 2000.
19. T. Kanungo, D. Mount, N. Netanyahu, C. Piatko, R. Silverman, and A. Wu, "An efficient k-means clustering algorithm: Analysis and implementation, " PAMI, 2002.
20. Y. Boykov and V. Kolmogorov, "An experimental comparison of mincut/ max- flow algorithms for energy minimization in vision, " PAMI, 2004.
21. D. Munoz, N. Vandapel, and M. Hebert, Onboard 3-D Point Cloud Classification with Associative Markov Networks. manuscript in preparation, 2009.
22. J. A. Bornstein and C. Shoemaker, "Army ground robotics research program, " Proceedings of the SPIE - The International Society for Optical Engineering, vol.5083, no.1, pp. 303 - 310, 2003.