About 1,960 results
|Conditional random fields are discriminatively trained and well suited to |
regularization. ... We present an empirical comparison between conditional
random fields and hidden Markov models for activity recognition using a robot tag
domain as well as ... We explore feature selection in a multi-robot activity
|R. Alami, S. Fleury, M. Herrb, F. Ingrand, F. Robert: Multi-Robot Cooperation in |
MARTHA Project. IEEE Robotics ... J. Canny: The Complexity of Robot Motion
Planning. ... R. Chellappa, A. Jain: Markov Random Fields: Theory and
|Butterfield, J., Jenkins, O., Gerkey, B.: Multi-robot markov random fields. In: |
Proceedings of the 7th International Joint Conference on Autonomous Agents
and Multiagent Systems, vol. 3, pp. 1211–1214. International Foundation for
|In [Thrun and Liu, 2003], the MRSLAM situation is solved using Sparse Extended |
Information Filter (SEIF) on which the maps and robots' poses are represented
using Gaussian Markov Random Fields. This decentralized approach focuses on
|Multi-robot SLAM with Sparse Extended Information Filers Sebastian Thrun1 and |
Yufeng Liu2 1 Department of Computer ... a sparse information filter technique,
which represents maps and robot poses by Gaussian Markov random fields.
|Visual event modeling and recognition using Markov logic networks. In |
Proceedings ... Feature selection for activity recognition in multi-robot domains. ...
Joint recognition of multiple concurrent activities using factorial conditional
|Brain Sci 21, 667–684 (1998) Chernova, S., Veloso, M.: Confidence-based multi-|
robot learning from demonstration. ... IEEE (2012) Edera, A., Bromberg, F.,
Schlüter, F.: Markov random fields factorization with context-specific
|Advances in Robotics, Vol.2 Manuel A. Armada, Alberto Sanfeliu, Manuel Ferre ... |
Celebi, A., Gullu, M., Erturk, S.: Mine detection in side scan sonar images using
markov random fields with brightness compensation. In: 2011 IEEE 19th ...
|Multi-robot control has also been investigated using probabilistic reinforcement |
learning approaches . ... More sophisticated probabilistic formulations in
computer vision include the use of Markov random fields 8 MACHINE LEARNING
|Balch, T., Arkin, R.C.: Behavior–based formation control for multirobot teams. |
IEEE Transactions on Robotics and ... Lecture Notes in Mathematics, pp. 28–43 (
1977) Rue, H., Held, L.: Gaussian Markov Random Fields: Theory and
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