Unsupervised probabilistic segmentation of motion data for mimesis modeling

2005 International Conference on Advanced Robotics, ICAR '05, Proceedings

Volumn 2005, Issue , 2005, Pages 411-417

  Janus, Bastien ^a   Nakamura, Yoshihiko ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; DATA REDUCTION; LEARNING SYSTEMS; MATHEMATICAL MODELS; MOTION ESTIMATION; VECTOR QUANTIZATION;

HUMANOID DEVELOPMENTS; INFORMATION TREATMENT SYSTEMS; VECTORIAL MOTION;

IMAGE SEGMENTATION;

EID: 33749042300     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICAR.2005.1507443     Document Type: Conference Paper

References (10)

1. Bilmes, J.(1998) A gentle tutorial on the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Technical Report ICSI-TR-97-021, University of Berkeley
2. Inamura, T., Tanie, H., Nakamura, Y. (2003) From Stochastic Motion Generation and Recognition to Geometric Symbol Development and Manipulation Proc of International Conference on Humanoid Robots(HUMANOIDS2003), 1b-02.
3. Inamura, T., Toshima, I., Nakamura, Y. (2002) Acquisition and Embodiment of Motion Elements in Closed Mimesis Loop Proc. of IEEE International Conference on Robotics and Automation (ICRA2002), Vol.2, pp.1539-1544.
4. Jordan, M. I., Jacobs, R. A. (1994) Hierarchical Mixtures of Experts and the EM Algorithm Neural Computation, 6, 181-214.
5. Tani, J., Nolfi, S. (1999) Learning to Perceive the World as Articulated: an Approach for Hierarchical Learning in Sensory-Motor Systems Neural Networks, special issue on organisation of computation in brain-like systems, Vol.12, pp.1131-1141.
6. Kohlmorgen, J. (2003) On Optimal Segmentation of Sequential Data NNSP 2003: Neural Networks for Signal Processing XIII, IEEE, NJ, 449-458.
7. Kohlmorgen, J., Lemm, S. (2002) A Dynamic HMM for Online Segmentation of Sequential Data NIPS 2001: Advances in Neural Information Processing Systems 14, (eds. T. G. Dietterich, S. Becker, and Z. Ghahramani), pp.793-800.
8. Muller, K.-R., Kohlmorgen, J., Pawelzik, K. (1995) Analysis of Switching Dynamics with Competing Neural Networks IEICE Trans. on Fundamentals of Electronics, Communications and Computer Se., E78-A, No.10, pp. 1306-1315.
9. Oliver, J. J., Forbes, C. S. (1997) Bayesian approaches to segmenting a simple time series. Technical Report 97/336, Dept. Computer Science, Monash University, Australia 3168.
10. Rabiner, L. R. (1989) A tutorial on Hidden Markov Models and selected Applications in Speech Recognition, Proc. IEEE, 77(2), pp.257-286.