Online, self-supervised terrain classification via discriminatively trained submodular Markov random fields

Proceedings - IEEE International Conference on Robotics and Automation

Volumn , Issue , 2008, Pages 2750-2757

  Vernaza, Paul ^a   Taskar, Ben ^a   Lee, Daniel D ^a  

^a UNIVERSITY OF PENNSYLVANIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); CLASSIFIERS; COPYING; HIDDEN MARKOV MODELS; INDUSTRIAL ENGINEERING; LEARNING SYSTEMS; ROBOTICS; ROBOTS; STRUCTURAL FRAMES;

INTERNATIONAL CONFERENCES; SUBMODULAR; TERRAIN CLASSIFICATION;

PIXELS;

EID: 51649124336     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ROBOT.2008.4543627     Document Type: Conference Paper

References (20)

1. S. Thrun, M. Montemerlo, H. Dahlkamp, and D. Stavens, "Stanley: The robot that won the DARPA Grand Challenge," Journal of Field Robotics, 2006.
2. B. Sofman, E. Lin, J. A. Bagnell, J. Cole, N. Vandapel, and A. Stentz, "Improving robot navigation through self-supervised online learning," Journal of Field Robotics, 2006.
3. I. Ulrich and I. Nourbakhsh, "Appearance-based obstacle detection with monocular color vision," in Proceedings of the AAAI National Conference on Artificial Intelligence, 2000.
4. J. Sun, T. Mehta, D. Wooden, M. Powers, J. Rehg, T. Balch, and M. Egerstedt, "Learning from examples in unstructured, outdoor environments," Journal of Field Robotics, 2006.
5. A. Howard, M. Turmon, L. Matthies, B. Tang, A. Angelova, and E. Mjolsness, "Towards learned traversability for robot navigation: from underfoot to the far field," Journal of Field Robotics, 2006.
6. C. Wellington, A. Courville, and A. Stentz, "Interacting Markov random fields for simultaneous terrain modeling and obstacle detection," in Proceedings of Robotics: Science and Systems I, 2005.
7. A. Torralba, K. P. Murphy, and W. T. Freeman, "Contextual models for object detection using boosted random fields," in Neural Information Processing Systems, 2004.
8. X. He, R. S. Zemel, and M. Á. Carreira-Perpiñán, "Multiscale conditional random fields for image labeling," in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004.
9. C. A. Bouman and M. Shapiro, "A multiscale random field model for Bayesian image segmentation," IEEE Transactions on Image Processing, March 1994.
10. V. Kolmogorov, "What energy functions can be minimized via graph cuts?" IEEE Transactions on Pattern Analysis and Machine Intelligence, Februrary 2004.
11. R. K. Ahuja, T. L. Magnanti, and J. B. Orlin, Network Flows: Theory, Algorithms, and Applications. Prentice Hall, 1993.
12. B. Taskar, V. Chatalbashev, and D. Koller, "Learning associative Markov networks," in Proceedings of the International Conference on Machine Learning, 2004.
13. N. D. Ratliff, J. A. Bagnell, and M. A. Zinkevich, "Maximum margin planning," in Proceedings of the 23rd International Conference on Machine Learning, 2006.
14. D. P. Bertsekas, Nonlinear Programming. Athena Scientific, 2003.
15. F. Rosenblatt, "The perceptron: a probabilistic model for information storage and organization in the brain," Psychological Review, 1958.
16. M. Collins, "Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms," in Proceedings of the Conference on Empirical Methods in Natural Language Processing, July 2002.
17. Y. Lin and D. D. Lee, "Bayesian L1-norm sparse learning," in International Conference on Acoustics, Speech, and Signal Processing, 2006.
18. M. A. Fischler and R. C. Bolles, "Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography," Communications of the ACM, 1981.
19. Y. Boykov and V. Kolmogorov, "An experimental comparison of mincut/ max-flow algorithms for energy minimization in vision," IEEE Transactions on Pattern Analysis and Machine Intelligence, September 2004.
20. K. Crammer and Y. Singer, "Ultraconservative online algorithms for multiclass problems," The Journal of Machine Learning Research, March 2003.