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CVIU
2006
76views more  CVIU 2006»
15 years 5 months ago
Homeostatic image perception: An artificial system
This paper describes how a visual system can automatically define features of interest from the observation of a large enough number of natural images. The principle complements t...
Thomas Feldman, Laurent Younes
CVPR
2009
IEEE
1081views Computer Vision» more  CVPR 2009»
17 years 20 days 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)
ECCV
2008
Springer
16 years 7 months ago
Learning for Optical Flow Using Stochastic Optimization
Abstract. We present a technique for learning the parameters of a continuousstate Markov random field (MRF) model of optical flow, by minimizing the training loss for a set of grou...
Yunpeng Li, Daniel P. Huttenlocher
CVPR
2009
IEEE
17 years 20 days ago
Learning Optimized MAP Estimates in Continuously-Valued MRF Models
We present a new approach for the discriminative training of continuous-valued Markov Random Field (MRF) model parameters. In our approach we train the MRF model by optimizing t...
Kegan G. G. Samuel, Marshall F. Tappen
MLMI
2007
Springer
15 years 11 months ago
Conditional Sequence Model for Context-Based Recognition of Gaze Aversion
Eye gaze and gesture form key conversational grounding cues that are used extensively in face-to-face interaction among people. To accurately recognize visual feedback during inter...
Louis-Philippe Morency, Trevor Darrell