Hyperdynamics Importance Sampling
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Principal Component Analysis over Continuous Subspaces and Intersection of Half-Spaces
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Matching and Embedding through Edit-Union of Trees
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Non-linear Bayesian Image Modelling
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part I
Partitioned Sampling, Articulated Objects, and Interface-Quality Hand Tracking
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
Learning Structural Variations in Shock Trees
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
A Novel Approach to Generate Multiple Shape Models for Tracking Applications
AMDO '02 Proceedings of the Second International Workshop on Articulated Motion and Deformable Objects
Unsupervised Parameterisation of Gaussian Mixture Models
CCIA '02 Proceedings of the 5th Catalonian Conference on AI: Topics in Artificial Intelligence
Object Tracking with an Adaptive Color-Based Particle Filter
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Multimodal Shape Tracking with Point Distribution Models
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Hand Tracking Using Spatial Gesture Modeling and Visual Feedback for a Virtual DJ System
ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces
Learning-based tracking of complex non-rigid motion
Journal of Computer Science and Technology
Adaptive Tracking of Non-Rigid Objects Based on Color Histograms and Automatic Parameter Selection
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
Incremental Model-Based Estimation Using Geometric Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analyzing and Capturing Articulated Hand Motion in Image Sequences
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based Hand Tracking Using a Hierarchical Bayesian Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Smart particle filtering for high-dimensional tracking
Computer Vision and Image Understanding
Robust facial feature tracking under varying face pose and facial expression
Pattern Recognition
Vision-based hand pose estimation: A review
Computer Vision and Image Understanding
A real-time hand tracker using variable-length Markov models of behaviour
Computer Vision and Image Understanding
Real-time 3-D human body tracking using learnt models of behaviour
Computer Vision and Image Understanding
Tracking with Dynamic Hidden-State Shape Models
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Fast mixing hyperdynamic sampling
Image and Vision Computing
Hand posture estimation in complex backgrounds by considering mis-match of model
ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
3D model-based hand tracking using stochastic direct search method
FGR' 04 Proceedings of the Sixth IEEE international conference on Automatic face and gesture recognition
Particle filtering with dynamic shape priors
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part I
Probabilistic spatio-temporal 2d-model for pedestrian motion analysis in monocular sequences
AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Robust 3d arm tracking from monocular videos
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part II
Robotics and Autonomous Systems
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Existing object tracking algorithms generally use some form of local optimisation, assuming that an object's position and shape change smoothly over time. In some situations this assumption is not valid: the trackable shape of an object may change discontinuously, for example if it is the 2D silhouette of a 3D object.In this paper we propose a novel method for modelling temporal shape discontinuities explicitly. Allowable shapes are represented as a union of (learned) bounded regions within a shape space. Discontinuous shape changes are described in terms of transitions between these regions. Transition probabilities are learned from training sequences and stored in a Markov model. In this way we can create "wormholes" in shape space. Tracking with such models is via an adaptation of the Condensation algorithm.