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» Modeling Image Textures by Gibbs Random Fields
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133
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ECCV
2008
Springer
16 years 5 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

Book
361views
17 years 1 months ago
Introduction to Statistical Signal Processing
"A random or stochastic process is a mathematical model for a phenomenon that evolves in time in an unpredictable manner from the viewpoint of the observer. The phenomenon m...
R.M. Gray
TASLP
2010
150views more  TASLP 2010»
14 years 10 months ago
Diffuse Reverberation Model for Efficient Image-Source Simulation of Room Impulse Responses
Abstract-- In many research fields of engineering and acoustics, the image-source model represents one of the most popular tools for the simulation of sound fields in virtual rever...
Eric A. Lehmann, Anders M. Johansson
ACRI
2006
Springer
15 years 7 months ago
Merging Cellular Automata for Simulating Surface Effects
This paper describes a model of three-dimensional cellular automata allowing to simulate different phenomena in the fields of computer graphics or image processing. Our method allo...
Stéphane Gobron, Denis Finck, Philippe Even...
112
Voted
ICIP
2003
IEEE
16 years 5 months ago
Unsupervised statistical sketching for non-photorealistic rendering models
This paper investigates the use of the Bayesian inference for devising an unsupervised sketch rendering procedure. As likelihood model of this inference, we exploit the recent sta...
Max Mignotte