21 search results - page 1 / 5 » A Global Perspective on MAP Inference for Low-Level Vision |
ICCV
2009
IEEE
14 years 9 months ago
2009
IEEE
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, ...
CVPR
2010
IEEE
14 years 26 days ago
2010
IEEE
Markov random fields (MRFs) are popular and generic probabilistic models of prior knowledge in low-level vision. Yet their generative properties are rarely examined, while applica...
CVPR
2010
IEEE
14 years 26 days ago
2010
IEEE
Maximum a posteriori (MAP) inference in Markov Random Fields (MRFs) is an NP-hard problem, and thus research has focussed on either finding efficiently solvable subclasses (e.g. t...
ICMCS
2007
IEEE
13 years 10 months ago
2007
IEEE
This paper proposes a novel method to enhance a driver’s situation awareness by dynamically providing a global view of surroundings for the driver. The surroundings of a vehicle...
CVPR
2008
IEEE
13 years 11 months ago
2008
IEEE
Second-order priors on the smoothness of 3D surfaces are a better model of typical scenes than first-order priors. However, stereo reconstruction using global inference algorithm...