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ICML
1999
IEEE
16 years 5 months ago
Monte Carlo Hidden Markov Models: Learning Non-Parametric Models of Partially Observable Stochastic Processes
We present a learning algorithm for non-parametric hidden Markov models with continuous state and observation spaces. All necessary probability densities are approximated using sa...
Sebastian Thrun, John Langford, Dieter Fox
STOC
2001
ACM
111views Algorithms» more  STOC 2001»
16 years 4 months ago
Optimal outlier removal in high-dimensional
We study the problem of finding an outlier-free subset of a set of points (or a probability distribution) in n-dimensional Euclidean space. As in [BFKV 99], a point x is defined t...
John Dunagan, Santosh Vempala
WACV
2005
IEEE
16 years 15 days ago
Detecting Motion Patterns via Direction Maps with Application to Surveillance
To facilitate accurate and efficient detection of motion patterns in video data, it is desirable to abstract from pixel intensity values to representations that explicitly and co...
Jacob M. Gryn, Richard P. Wildes, John K. Tsotsos
CVPR
2010
IEEE
16 years 13 days ago
On Growth and Formlets: Sparse Multi-Scale Coding of Planar Shape
This paper presents a sparse representation of 2D planar shape through the composition of warping functions, termed formlets, localized in scale and space. Each formlet subjects t...
Timothy Oleskiw, James Elder, Gabriel Peyr
SCALESPACE
2009
Springer
15 years 11 months ago
An Elasticity Approach to Principal Modes of Shape Variation
Abstract. Concepts from elasticity are applied to analyze modes of variation on shapes in two and three dimensions. This approach represents a physically motivated alternative to s...
Martin Rumpf, Benedikt Wirth