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» Inference for Multiplicative Models
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158
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FTCGV
2011
122views more  FTCGV 2011»
14 years 7 months ago
Structured Learning and Prediction in Computer Vision
Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structur...
Sebastian Nowozin, Christoph H. Lampert
197
Voted
ICCV
2003
IEEE
16 years 5 months ago
Comparison of Graph Cuts with Belief Propagation for Stereo, using Identical MRF Parameters
Recent stereo algorithms have achieved impressive results by modelling the disparity image as a Markov Random Field (MRF). An important component of an MRF-based approach is the i...
Marshall F. Tappen, William T. Freeman
132
Voted
MICAI
2004
Springer
15 years 9 months ago
Extracting Temporal Patterns from Time Series Data Bases for Prediction of Electrical Demand
In this paper we present a technique for prediction of electrical demand based on multiple models. The multiple models are composed by several local models, each one describing a r...
J. Jesus Rico Melgoza, Juan J. Flores, Constantino...
148
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ACAL
2009
Springer
15 years 10 months ago
Evaluation of the Effectiveness of Machine-Based Situation Assessment
The Information Fusion Panel within The Technical Cooperation Program (TTCP) is developing algorithms to perform machine-based situation assessment to assist human operators in co...
David M. Lingard, Dale A. Lambert
206
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SMA
2010
ACM
181views Solid Modeling» more  SMA 2010»
14 years 10 months ago
Threshold selection in jump-discriminant filter for discretely observed jump processes
Threshold estimation is one of the useful techniques in the inference for jump-type stochastic processes from discrete observations. In this method, a jump-discriminant filter is ...
Yasutaka Shimizu