메뉴 건너뛰기




Volumn 77, Issue 1-3, 2008, Pages 103-124

Learning and inferring motion patterns using parametric segmental switching linear dynamic systems

Author keywords

Behavior recognition; Biology; MCMC; Probabilistic graphical models; Time series; Trajectory analysis

Indexed keywords

GRAPHIC METHODS; LEARNING SYSTEMS; LINEAR SYSTEMS; MATHEMATICAL MODELS; STATE SPACE METHODS; TIME SERIES ANALYSIS;

EID: 39749137487     PISSN: 09205691     EISSN: 15731405     Source Type: Journal    
DOI: 10.1007/s11263-007-0062-z     Document Type: Article
Times cited : (130)

References (57)
  • 2
    • 33846994504 scopus 로고    scopus 로고
    • How A.I. and multi-robot systems research will accelerate our understanding of social animal behavior
    • 7
    • Balch, T., Dellaert, F., Feldman, A., Guillory, A., Isbell, C., Khan, Z., Stein, A., & Wilde, H. (2006). How A.I. and multi-robot systems research will accelerate our understanding of social animal behavior. Proceedings of IEEE, 94(7), 1145-1463.
    • (2006) Proceedings of IEEE , vol.94 , pp. 1145-1463
    • Balch, T.1    Dellaert, F.2    Feldman, A.3    Guillory, A.4    Isbell, C.5    Khan, Z.6    Stein, A.7    Wilde, H.8
  • 3
    • 0034833475 scopus 로고    scopus 로고
    • Automatically tracking and analyzing the behavior of live insect colonies
    • Montreal
    • Balch, T., Khan, Z., & Veloso, M. (2001). Automatically tracking and analyzing the behavior of live insect colonies. In Proceedings autonomous agents (pp. 521-528), Montreal.
    • (2001) Proceedings Autonomous Agents , pp. 521-528
    • Balch, T.1    Khan, Z.2    Veloso, M.3
  • 6
    • 0016552254 scopus 로고
    • Tracking in a cluttered environment with probabilistic data-association
    • Bar-Shalom, Y., & Tse, E. (1975). Tracking in a cluttered environment with probabilistic data-association. Automatica, 11, 451-460.
    • (1975) Automatica , vol.11 , pp. 451-460
    • Bar-Shalom, Y.1    Tse, E.2
  • 11
    • 0000761439 scopus 로고    scopus 로고
    • Markov chain Monte Carlo in conditionally Gaussian state spaece models
    • Carter, C., & Kohn, R. (1996). Markov chain Monte Carlo in conditionally Gaussian state spaece models. Biometrika, 83, 589-601.
    • (1996) Biometrika , vol.83 , pp. 589-601
    • Carter, C.1    Kohn, R.2
  • 12
    • 0002205556 scopus 로고    scopus 로고
    • Rao-Blackwellisation of sampling schemes
    • 1
    • Casella, G., & Robert, C. P. (1996). Rao-Blackwellisation of sampling schemes. Biometrika, 83(1), 81-94.
    • (1996) Biometrika , vol.83 , pp. 81-94
    • Casella, G.1    Robert, C.P.2
  • 14
    • 0036571002 scopus 로고    scopus 로고
    • An MCMC sampling approach to estimation of nonstationary hidden Markov Models
    • 5
    • Djuric, P. M., & Chun, J.-H. (2002). An MCMC sampling approach to estimation of nonstationary hidden Markov Models. IEEE Transactions on Signal Processing, 50(5), 1113-1123.
    • (2002) IEEE Transactions on Signal Processing , vol.50 , pp. 1113-1123
    • Djuric, P.M.1    Chun, J.-H.2
  • 16
    • 0035369663 scopus 로고    scopus 로고
    • Iterative algorithms for state estimation of jump Markov linear systems
    • 6
    • Doucet, A., & Andrieu, C. (2001). Iterative algorithms for state estimation of jump Markov linear systems. IEEE Transactions on Signal Processing, 49(6), 1216-1227.
    • (2001) IEEE Transactions on Signal Processing , vol.49 , pp. 1216-1227
    • Doucet, A.1    Andrieu, C.2
  • 18
    • 12344281467 scopus 로고    scopus 로고
    • Representing honey bee behavior for recognition using human trainable models
    • Feldman, A., & Balch, T. (2004). Representing honey bee behavior for recognition using human trainable models. Adaptive Behavior, 12, 241-250.
    • (2004) Adaptive Behavior , vol.12 , pp. 241-250
    • Feldman, A.1    Balch, T.2
  • 23
    • 0001646121 scopus 로고    scopus 로고
    • Variational learning for switching state-space models
    • 4
    • Ghahramani, Z., & Hinton, G. E. (1998). Variational learning for switching state-space models. Neural Computation, 12(4), 963-996.
    • (1998) Neural Computation , vol.12 , pp. 963-996
    • Ghahramani, Z.1    Hinton, G.E.2
  • 25
    • 77956890234 scopus 로고
    • Monte Carlo sampling methods using Markov chains and their applications
    • Hastings, W. K. (1970). Monte Carlo sampling methods using Markov chains and their applications. Biometrika, 57, 97-109.
    • (1970) Biometrika , vol.57 , pp. 97-109
    • Hastings, W.K.1
  • 28
    • 33947099988 scopus 로고    scopus 로고
    • MCMC data association and sparse factorization updating for real time multitarget tracking with merged and multiple measurements
    • 12
    • Khan, Z., Balch, T., & Dellaert, F. (2006). MCMC data association and sparse factorization updating for real time multitarget tracking with merged and multiple measurements. IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(12), 1960-1972.
    • (2006) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.28 , pp. 1960-1972
    • Khan, Z.1    Balch, T.2    Dellaert, F.3
  • 29
    • 0002634803 scopus 로고
    • Dynamic linear models with Markov-switching
    • 1-2
    • Kim, C.-J. (1994). Dynamic linear models with Markov-switching. Journal of Econometrics, 60(1-2), 1-22.
    • (1994) Journal of Econometrics , vol.60 , pp. 1-22
    • Kim, C.-J.1
  • 30
    • 33745170376 scopus 로고    scopus 로고
    • Segmental Hidden Markov Models with Random Effects for Waveform Modeling
    • Kim, S., & Smyth, P. (2006). Segmental Hidden Markov Models with Random Effects for Waveform Modeling. Journal of Machine Learning Research, 7, 945-969.
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 945-969
    • Kim, S.1    Smyth, P.2
  • 32
    • 0006236307 scopus 로고    scopus 로고
    • Inference in hybrid networks: Theoretical limits and practical algorithms
    • Seattle, WA, August 2001
    • Lerner, U., & Parr, R. (2001). Inference in hybrid networks: theoretical limits and practical algorithms. In Proceedings of the 17th conference on uncertainty in AI (UAI) (pp. 310-318), Seattle, WA, August 2001.
    • (2001) Proceedings of the 17th Conference on Uncertainty in AI (UAI) , pp. 310-318
    • Lerner, U.1    Parr, R.2
  • 34
    • 0022685753 scopus 로고
    • Continuously variable duration hidden Markov models for automatic speech recognition
    • 1
    • Levinson, S. E. (1990). Continuously variable duration hidden Markov models for automatic speech recognition. Computer Speech and Language, 1(1), 29-45.
    • (1990) Computer Speech and Language , vol.1 , pp. 29-45
    • Levinson, S.E.1
  • 48
    • 77957148776 scopus 로고    scopus 로고
    • Data driven MCMC for appearance-based topological mapping
    • Ranganathan, A., & Dellaert, F. (2005). Data driven MCMC for appearance-based topological mapping. In Robotics: science and systems I (pp. 209-216).
    • (2005) Robotics: Science and Systems i , pp. 209-216
    • Ranganathan, A.1    Dellaert, F.2
  • 51
    • 0033556862 scopus 로고    scopus 로고
    • A unifying review of linear Gaussian models
    • 2
    • Roweis, S., & Ghahramani, Z. (1999). A unifying review of linear Gaussian models. Neural Computation, 11(2), 305-345.
    • (1999) Neural Computation , vol.11 , pp. 305-345
    • Roweis, S.1    Ghahramani, Z.2
  • 57
    • 0142103260 scopus 로고    scopus 로고
    • Hierarchical visualization of time-series data using switching linear dynamical systems
    • 10
    • Zoeter, O., & Heskes, T. (2003). Hierarchical visualization of time-series data using switching linear dynamical systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25(10), 1202-1215.
    • (2003) IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.25 , pp. 1202-1215
    • Zoeter, O.1    Heskes, T.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.