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» Probability Models for High Dynamic Range Imaging
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ICCV
2009
IEEE
16 years 2 months ago
A Global Perspective on MAP Inference for Low-Level Vision
In recent years the Markov Random Field (MRF) has become the de facto probabilistic model for low-level vision applications. However, in a maximum a posteriori (MAP) framework, ...
Oliver J. Woodford, Carsten Rother, Vladimir Kolmo...
3DPVT
2006
IEEE
163views Visualization» more  3DPVT 2006»
15 years 3 months ago
Vanishing Hull
Vanishing points are valuable in many vision tasks such as orientation estimation, pose recovery and 3D reconstruction from a single image. Many methods have been proposed to addr...
Jinhui Hu, Suya You, Ulrich Neumann
ICCV
2005
IEEE
15 years 3 months ago
On the Spatial Statistics of Optical Flow
We develop a method for learning the spatial statistics of optical flow fields from a novel training database. Training flow fields are constructed using range images of natur...
Stefan Roth, Michael J. Black
CORR
2010
Springer
152views Education» more  CORR 2010»
14 years 9 months ago
Deploying Wireless Networks with Beeps
Abstract. We present the discrete beeping communication model, which assumes nodes have minimal knowledge about their environment and severely limited communication capabilities. S...
Alejandro Cornejo, Fabian Kuhn
ICPR
2008
IEEE
15 years 4 months ago
Face super-resolution using 8-connected Markov Random Fields with embedded prior
In patch based face super-resolution method, the patch size is usually very small, and neighbor patches’ relationship via overlapped regions is only to keep smoothness of recons...
Kai Guo, Xiaokang Yang, Rui Zhang, Guangtao Zhai, ...