International Journal of Computer Vision
International Journal of Computer Vision - Special issue on statistical and computational theories of vision: Part II
Statistics and Computing
Building Roadmaps of Local Minima of Visual Models
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
How Does CONDENSATION Behave with a Finite Number of Samples?
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
Stochastic Tracking of 3D Human Figures Using 2D Image Motion
ECCV '00 Proceedings of the 6th European Conference on Computer Vision-Part II
3-D model-based tracking of humans in action: a multi-view approach
CVPR '96 Proceedings of the 1996 Conference on Computer Vision and Pattern Recognition (CVPR '96)
Singularity Analysis for Articulated Object Tracking
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Global and local deformations of solid primitives
SIGGRAPH '84 Proceedings of the 11th annual conference on Computer graphics and interactive techniques
Tracking through Singularities and Discontinuities by Random Sampling
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Consistency and Coupling in Human Model Likelihoods
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Wormholes in Shape Space: Tracking through Discontinuous Changes in Shape
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Building Roadmaps of Local Minima of Visual Models
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Building Roadmaps of Minima and Transitions in Visual Models
International Journal of Computer Vision
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Navigating in virtual environments using a vision-based interface
Proceedings of the third Nordic conference on Human-computer interaction
Recovering 3D Human Body Configurations Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational studies of human motion: part 1, tracking and motion synthesis
Foundations and Trends® in Computer Graphics and Vision
Partial Linear Gaussian Models for Tracking in Image Sequences Using Sequential Monte Carlo Methods
International Journal of Computer Vision
Robust Object Tracking Via Online Dynamic Spatial Bias Appearance Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accurate appearance-based Bayesian tracking for maneuvering targets
Computer Vision and Image Understanding
Exploiting motion correlations in 3-D articulated human motion tracking
IEEE Transactions on Image Processing
Fast mixing hyperdynamic sampling
Image and Vision Computing
Twin Gaussian Processes for Structured Prediction
International Journal of Computer Vision
Cyclic articulated human motion tracking by sequential ancestral simulation
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
Kinematic jump processes for monocular 3D human tracking
CVPR'03 Proceedings of the 2003 IEEE computer society conference on Computer vision and pattern recognition
Generalized darting Monte Carlo
Pattern Recognition
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Sequential random sampling ('Markov Chain Monte-Carlo') is a popular strategy for many vision problems involving multimodal distributions over high-dimensional parameter spaces. It applies both to importance sampling (where one wants to sample points according to their 'importance' for some calculation, but otherwise fairly) and to global optimization (where one wants to find good minima, or at least good starting points for local minimization, regardless of fairness). Unfortunately, most sequential samplers are very prone to becoming 'trapped' for long periods in unrepresentative local minima, which leads to biased or highly variable estimates. We present a general strategy for reducing MCMC trapping that generalizes Voter's 'hyperdynamic sampling' from computational chemistry. The local gradient and curvature of the input distribution are used to construct an adaptive importance sampler that focuses samples on low cost negative curvature regions likely to contain 'transition states' -- codimension-1 saddle points representing 'mountain passes' connecting adjacent cost basins. This substantially accelerates inter-basin transition rates while still preserving correct relative transition probabilities. Experimental tests on the difficult problem of 3D articulated human pose estimation from monocular images show significantly enhanced minimum exploration.