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» Estimating Markov Random Field Potentials for Natural Images
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CVPR
2009
IEEE
1382views Computer Vision» more  CVPR 2009»
15 years 2 months ago
Super-Resolution via Recapture and Bayesian Effect Modeling
This paper presents Bayesian edge inference (BEI), a single-frame super-resolution method explicitly grounded in Bayesian inference that addresses issues common to existing meth...
Bryan S. Morse, Dan Ventura, Kevin D. Seppi, Neil ...
CIMAGING
2009
145views Hardware» more  CIMAGING 2009»
13 years 8 months ago
Wavelet-based Poisson rate estimation using the Skellam distribution
Owing to the stochastic nature of discrete processes such as photon counts in imaging, real-world data measurements often exhibit heteroscedastic behavior. In particular, time ser...
Keigo Hirakawa, Farhan A. Baqai, Patrick J. Wolfe
TMI
2010
133views more  TMI 2010»
13 years 6 months ago
Synthetic Magnetic Resonance Imaging Revisited
—Synthetic magnetic resonance (MR) imaging is an approach suggested in the literature to predict MR images at different design parameter settings from at least three observed MR ...
Ranjan Maitra, John J. Riddles
ECCV
1998
Springer
14 years 9 months ago
A Two-Stage Probabilistic Approach for Object Recognition
Assume that some objects are present in an image but can be seen only partially and are overlapping each other. To recognize the objects, we have to rstly separate the objects from...
Stan Z. Li, Joachim Hornegger
PAMI
2008
137views more  PAMI 2008»
13 years 7 months ago
IRGS: Image Segmentation Using Edge Penalties and Region Growing
This paper proposes an image segmentation method named iterative region growing using semantics (IRGS), which is characterized by two aspects. First, it uses graduated increased ed...
Qiyao Yu, David A. Clausi