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CVPR
2004
IEEE
14 years 9 months ago
Incremental Density Approximation and Kernel-Based Bayesian Filtering for Object Tracking
Statistical density estimation techniques are used in many computer vision applications such as object tracking, background subtraction, motion estimation and segmentation. The pa...
Bohyung Han, Dorin Comaniciu, Ying Zhu, Larry S. D...
ICCV
1999
IEEE
14 years 9 months ago
Probabilistic Detection and Tracking of Motion Discontinuities
We propose a Bayesian framework for representing and recognizing local image motion in terms of two primitive models: translation and motion discontinuity. Motion discontinuities ...
Michael J. Black, David J. Fleet
BMCBI
2010
110views more  BMCBI 2010»
13 years 7 months ago
A random effect multiplicative heteroscedastic model for bacterial growth
Background: Predictive microbiology develops mathematical models that can predict the growth rate of a microorganism population under a set of environmental conditions. Many prima...
Ricardo Cao, Mario Francisco-Fernández, Emi...
CSDA
2010
111views more  CSDA 2010»
13 years 7 months ago
Mixtures of regressions with predictor-dependent mixing proportions
We extend the standard mixture of linear regressions model by allowing mixing proportions to be modeled nonparametrically as a function of the predictors. This framework allows fo...
D. S. Young, D. R. Hunter
CVPR
2008
IEEE
14 years 9 months ago
Learning for stereo vision using the structured support vector machine
We present a random field based model for stereo vision with explicit occlusion labeling in a probabilistic framework. The model employs non-parametric cost functions that can be ...
Yunpeng Li, Daniel P. Huttenlocher