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» A Conditional Random Field Model for Video Super-resolution
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CVPR
2009
IEEE
1081views Computer Vision» more  CVPR 2009»
16 years 7 months ago
Learning Real-Time MRF Inference for Image Denoising
Many computer vision problems can be formulated in a Bayesian framework with Markov Random Field (MRF) or Conditional Random Field (CRF) priors. Usually, the model assumes that ...
Adrian Barbu (Florida State University)
NIPS
2001
15 years 1 months ago
Linking Motor Learning to Function Approximation: Learning in an Unlearnable Force Field
Reaching movements require the brain to generate motor commands that rely on an internal model of the task's dynamics. Here we consider the errors that subjects make early in...
O. Donchin, Reza Shadmehr
AAAI
2008
15 years 2 months ago
Learning to Analyze Binary Computer Code
We present a novel application of structured classification: identifying function entry points (FEPs, the starting byte of each function) in program binaries. Such identification ...
Nathan E. Rosenblum, Xiaojin Zhu, Barton P. Miller...
MM
2006
ACM
221views Multimedia» more  MM 2006»
15 years 5 months ago
Video object segmentation by motion-based sequential feature clustering
Segmentation of video foreground objects from background has many important applications, such as human computer interaction, video compression, multimedia content editing and man...
Mei Han, Wei Xu, Yihong Gong
NAACL
2010
14 years 9 months ago
Investigations into the Crandem Approach to Word Recognition
We suggest improvements to a previously proposed framework for integrating Conditional Random Fields and Hidden Markov Models, dubbed a Crandem system (2009). The previous authors...
Rohit Prabhavalkar, Preethi Jyothi, William Hartma...