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TIP
2008
175views more  TIP 2008»
13 years 6 months ago
Algorithmic and Architectural Optimizations for Computationally Efficient Particle Filtering
Abstract--In this paper, we analyze the computational challenges in implementing particle filtering, especially to video sequences. Particle filtering is a technique used for filte...
Aswin C. Sankaranarayanan, Ankur Srivastava, Rama ...
ICIP
2007
IEEE
14 years 8 months ago
Monocular Tracking 3D People By Gaussian Process Spatio-Temporal Variable Model
Tracking 3D people from monocular video is often poorly constrained. To mitigate this problem, prior knowledge should be exploited. In this paper, the Gaussian process spatio-temp...
Junbiao Pang, Laiyun Qing, Qingming Huang, Shuqian...
JMLR
2010
184views more  JMLR 2010»
13 years 1 months ago
Sequential Monte Carlo Samplers for Dirichlet Process Mixtures
In this paper, we develop a novel online algorithm based on the Sequential Monte Carlo (SMC) samplers framework for posterior inference in Dirichlet Process Mixtures (DPM) (DelMor...
Yener Ülker, Bilge Günsel, Ali Taylan Ce...
DAGM
2010
Springer
13 years 7 months ago
Gaussian Mixture Modeling with Gaussian Process Latent Variable Models
Density modeling is notoriously difficult for high dimensional data. One approach to the problem is to search for a lower dimensional manifold which captures the main characteristi...
Hannes Nickisch, Carl Edward Rasmussen
JMLR
2010
155views more  JMLR 2010»
13 years 1 months ago
Bayesian Gaussian Process Latent Variable Model
We introduce a variational inference framework for training the Gaussian process latent variable model and thus performing Bayesian nonlinear dimensionality reduction. This method...
Michalis Titsias, Neil D. Lawrence