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» Application of Level Set Methods in Computer Vision
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CVPR
2009
IEEE
1081views Computer Vision» more  CVPR 2009»
17 years 1 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)
CVPR
2007
IEEE
16 years 8 months ago
Spatio-Temporal Markov Random Field for Video Denoising
This paper presents a novel spatio-temporal Markov random field (MRF) for video denoising. Two main issues are addressed in this paper, namely, the estimation of noise model and t...
Jia Chen, Chi-Keung Tang
HCI
2009
15 years 4 months ago
Estimating Productivity: Composite Operators for Keystroke Level Modeling
Task time is a measure of productivity in an interface. Keystroke Level Modeling (KLM) can predict experienced user task time to within 10 to 30% of actual times. One of the bigges...
Jeff Sauro
CVPR
2011
IEEE
15 years 1 months ago
What You Saw is Not What You Get: Domain Adaptation Using Asymmetric Kernel Transforms
In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features...
Brian Kulis, Kate Saenko, Trevor Darrell
ISORC
2003
IEEE
15 years 11 months ago
Object-Oriented Middleware Infrastructure for Distributed Augmented Reality
The paper describes design and implementation of software infrastructure for building augmented reality applications for ubiquitous computing environments. Augmented reality is on...
Eiji Tokunaga, Andrej van der Zee, Makoto Kurahash...