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» An LFT approach to parameter estimation
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ML
1998
ACM
139views Machine Learning» more  ML 1998»
13 years 10 months ago
The Hierarchical Hidden Markov Model: Analysis and Applications
We introduce, analyze and demonstrate a recursive hierarchical generalization of the widely used hidden Markov models, which we name Hierarchical Hidden Markov Models (HHMM). Our m...
Shai Fine, Yoram Singer, Naftali Tishby
PAMI
2008
161views more  PAMI 2008»
13 years 9 months ago
Multilayered 3D LiDAR Image Construction Using Spatial Models in a Bayesian Framework
Standard 3D imaging systems process only a single return at each pixel from an assumed single opaque surface. However, there are situations when the laser return consists of multip...
Sergio Hernandez-Marin, Andrew M. Wallace, Gavin J...
TIP
2010
127views more  TIP 2010»
13 years 8 months ago
Bayesian Compressive Sensing Using Laplace Priors
In this paper we model the components of the compressive sensing (CS) problem, i.e., the signal acquisition process, the unknown signal coefficients and the model parameters for ...
S. Derin Babacan, Rafael Molina, Aggelos K. Katsag...
ICCV
2011
IEEE
12 years 10 months ago
Incremental On-line Semi-supervised Learning for Segmenting the Left Ventricle of the Heart from Ultrasound Data
Recently, there has been an increasing interest in the investigation of statistical pattern recognition models for the fully automatic segmentation of the left ventricle (LV) of t...
Gustavo Carneiro, Jacinto C. Nascimento
CVPR
2004
IEEE
15 years 7 days ago
Atlanta World: An Expectation Maximization Framework for Simultaneous Low-Level Edge Grouping and Camera Calibration in Complex
Edges in man-made environments, grouped according to vanishing point directions, provide single-view constraints that have been exploited before as a precursor to both scene under...
Grant Schindler, Frank Dellaert