Continuously variable duration hidden Markov models for automatic speech recognition
Computer Speech and Language
Tracking and data association
A unifying review of linear Gaussian models
Neural Computation
Parametric Hidden Markov Models for Gesture Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Deformable Markov model templates for time-series pattern matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Learning and Classification of Complex Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatically tracking and analyzing the behavior of live insect colonies
Proceedings of the fifth international conference on Autonomous agents
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Motion texture: a two-level statistical model for character motion synthesis
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
Transformation-Invariant Clustering Using the EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inference in Hybrid Networks: Theoretical Limits and Practical Algorithms
UAI '01 Proceedings of the 17th Conference in Uncertainty in Artificial Intelligence
Bayesian Fault Detection and Diagnosis in Dynamic Systems
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Representing Honey Bee Behavior for Recognition Using Human Trainable Models
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Tracking Multiple Mouse Contours (without Too Many Samples)
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Generalizing Swendsen-Wang to Sampling Arbitrary Posterior Probabilities
IEEE Transactions on Pattern Analysis and Machine Intelligence
A data-driven approach to quantifying natural human motion
ACM SIGGRAPH 2005 Papers
Learning and Inference in Parametric Switching Linear Dynamical Systems
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Variational Learning for Switching State-Space Models
Neural Computation
A Model-Based Approach for Estimating Human 3D Poses in Static Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Segmental Hidden Markov Models with Random Effects for Waveform Modeling
The Journal of Machine Learning Research
Data-driven MCMC for learning and inference in switching linear dynamic systems
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
A Rao-Blackwellized particle filter for EigenTracking
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
An MCMC sampling approach to estimation of nonstationary hiddenMarkov models
IEEE Transactions on Signal Processing
Iterative algorithms for state estimation of jump Markov linearsystems
IEEE Transactions on Signal Processing
Particle filters for state estimation of jump Markov linear systems
IEEE Transactions on Signal Processing
Hierarchical visualization of time-series data using switching linear dynamical systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised view and rate invariant clustering of video sequences
Computer Vision and Image Understanding
Identifying fusion events in fluorescence microscopy images
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Human action recognition using boosted EigenActions
Image and Vision Computing
From dance to touch: movement qualities for interaction design
CHI '11 Extended Abstracts on Human Factors in Computing Systems
Approximate posterior distributions for convolutional two-level hidden Markov models
Computational Statistics & Data Analysis
Max-Margin Early Event Detectors
International Journal of Computer Vision
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Switching Linear Dynamic System (SLDS) models are a popular technique for modeling complex nonlinear dynamic systems. An SLDS can describe complex temporal patterns more concisely and accurately than an HMM by using continuous hidden states. However, the use of SLDS models in practical applications is challenging for three reasons. First, exact inference in SLDS models is computationally intractable. Second, the geometric duration model induced in standard SLDSs limits their representational power. Third, standard SLDSs do not provide a principled way to interpret systematic variations governed by higher order parameters.The contributions in this paper address all of these three challenges. First, we present a data-driven MCMC (DD-MCMC) sampling method for approximate inference in SLDSs. We show DD-MCMC provides an efficient method for estimation and learning in SLDS models. Second, we present segmental SLDSs (S-SLDS), where the geometric distributions of the switching state durations are replaced with arbitrary duration models. Third, we extend the standard SLDS model with additional global parameters that can capture systematic temporal and spatial variations. The resulting parametric SLDS model (P-SLDS) uses EM to robustly interpret parametrized motions by incorporating additional global parameters that underly systematic variations of the overall motion.The overall development of the extensions for SLDSs provide a principled framework to interpret complex motions. The framework is applied to the honey bee dance interpretation task in the context of the on-going BioTracking project at the Georgia Institute of Technology. The experimental results suggest that the enhanced models provide an effective framework for a wide range of motion analysis applications.