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» Recognition Model with Extension Fields
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IJCAI
2003
15 years 19 days ago
A General Model for Online Probabilistic Plan Recognition
We present a new general framework for online istic plan recognition called the Abstract Hidden Markov Memory Model (AHMEM). The l is an extension of the existing Abstract Hidden ...
Hung Hai Bui
DFT
2006
IEEE
120views VLSI» more  DFT 2006»
15 years 5 months ago
On-Line Mapping of In-Field Defects in Image Sensor Arrays
Continued increase in complexity of digital image sensors means that defects are more likely to develop in the field, but little concrete information is available on in-field defe...
Jozsef Dudas, Cory Jung, Linda Wu, Glenn H. Chapma...
JMLR
2008
230views more  JMLR 2008»
14 years 11 months ago
Exponentiated Gradient Algorithms for Conditional Random Fields and Max-Margin Markov Networks
Log-linear and maximum-margin models are two commonly-used methods in supervised machine learning, and are frequently used in structured prediction problems. Efficient learning of...
Michael Collins, Amir Globerson, Terry Koo, Xavier...
EVENT
2001
139views more  EVENT 2001»
15 years 20 days ago
Hierarchical Motion History Images for Recognizing Human Motion
There has been a recent and increasing interest in computer analysis and recognition of human motion. Previously we presented an efficient real-time approach for representing huma...
James W. Davis
CVPR
2007
IEEE
16 years 1 months ago
Latent-Dynamic Discriminative Models for Continuous Gesture Recognition
Many problems in vision involve the prediction of a class label for each frame in an unsegmented sequence. In this paper, we develop a discriminative framework for simultaneous se...
Louis-Philippe Morency, Ariadna Quattoni, Trevor D...