Practical methods of optimization; (2nd ed.)
Practical methods of optimization; (2nd ed.)
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
Statistics and Computing
Training products of experts by minimizing contrastive divergence
Neural Computation
Hyperdynamics Importance Sampling
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Building Roadmaps of Local Minima of Visual Models
ECCV '02 Proceedings of the 7th 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
Proposal maps driven MCMC for estimating human body pose in static images
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
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One of the main shortcomings of Markov chain Monte Carlo samplers is their inability to mix between modes of the target distribution. In this paper we show that advance knowledge of the location of these modes can be incorporated into the MCMC sampler by introducing mode-hopping moves that satisfy detailed balance. The proposed sampling algorithm explores local mode structure through local MCMC moves (e.g. diffusion or Hybrid Monte Carlo) but in addition also represents the relative strengths of the different modes correctly using a set of global moves. This 'mode-hopping' MCMC sampler can be viewed as a generalization of the darting method [1]. We illustrate the method on learning Markov random fields and evaluate it against the spherical darting algorithm on a 'real world' vision application of inferring 3D human body pose distributions from 2D image information.