EMMCVPR
2011
Springer
12 years 11 months ago
2011
Springer
Traditional approaches to Multiple-Instance Learning (MIL) operate under the assumption that the instances of a bag are generated independently, and therefore typically learn an in...
ICCV
2011
IEEE
12 years 12 months ago
2011
IEEE
Many state-of-the-art segmentation algorithms rely on Markov or Conditional Random Field models designed to enforce spatial and global consistency constraints. This is often accom...
SIAMIS
2010
13 years 6 months ago
2010
Markov random field (MRF) models, including conditional random field models, are popular in computer vision. However, in order to be computationally tractable, they are limited to ...
ICPR
2006
IEEE
15 years 28 days ago
2006
IEEE
We address in this paper the problem of segmenting complex handritten pages such as novelist drafts or authorial manuscripts. We propose to use stochastic and contextual models in...
ICCV
2007
IEEE
15 years 1 months ago
2007
IEEE
In contrast to traditional Markov random field (MRF) models, we develop a Steerable Random Field (SRF) in which the field potentials are defined in terms of filter responses that ...
CVPR
2008
IEEE
15 years 1 months ago
2008
IEEE
We present a random field based model for stereo vision with explicit occlusion labeling in a probabilistic framework. The model employs non-parametric cost functions that can be ...
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CVPR
2009
IEEE
15 years 7 months ago
2009
IEEE
Markov random field (MRF, CRF) models are popular in
computer vision. However, in order to be computationally
tractable they are limited to incorporate only local interactions
a...