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» Markov Random Field Modeling in Computer Vision
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FOCS
2000
IEEE
14 years 1 months ago
The Randomness Recycler: A New Technique for Perfect Sampling
For many probability distributions of interest, it is quite difficult to obtain samples efficiently. Often, Markov chains are employed to obtain approximately random samples fro...
James Allen Fill, Mark Huber
CVPR
2007
IEEE
14 years 11 months ago
Unsupervised Segmentation of Objects using Efficient Learning
We describe an unsupervised method to segment objects detected in images using a novel variant of an interest point template, which is very efficient to train and evaluate. Once a...
Himanshu Arora, Nicolas Loeff, David A. Forsyth, N...
WSCG
2001
167views more  WSCG 2001»
13 years 10 months ago
Component-Based Architectures for Computer Vision Systems
Research performed in the field of computer vision has steadily ignored recent advances in programming tools and techniques, relying on well-established traditional methods, such ...
A. Economopoulos, Drakoulis Martakos
CVPR
2008
IEEE
14 years 11 months ago
Structure learning in random fields for heart motion abnormality detection
Coronary Heart Disease can be diagnosed by assessing the regional motion of the heart walls in ultrasound images of the left ventricle. Even for experts, ultrasound images are dif...
Glenn Fung, Kevin Murphy, Mark Schmidt, Róm...
ICPR
2008
IEEE
14 years 3 months ago
Edge-preserving unscented Kalman filter for speckle reduction
We propose a recursive spatial-domain speckle reduction algorithm for synthetic aperture radar (SAR) imagery based on the unscented Kalman filter (UKF) with a discontinuity-adapt...
Gorthi R. K. Sai Subrahmanyam, A. N. Rajagopalan, ...