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EMMCVPR
2011
Springer
7 years 7 months ago
Multiple-Instance Learning with Structured Bag Models
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...
Jonathan Warrell, Philip H. S. Torr
ICCV
2011
IEEE
7 years 7 months ago
Are Spatial and Global Constraints Really Necessary for Segmentation?
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...
Aurelien Lucchi, Yunpeng Li, Xavier Boix, Kevin Sm...
SIAMIS
2010
378views more  SIAMIS 2010»
8 years 2 months ago
Global Interactions in Random Field Models: A Potential Function Ensuring Connectedness
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 ...
Sebastian Nowozin, Christoph H. Lampert
ICPR
2006
IEEE
9 years 8 months ago
A Markovian Approach for Handwritten Document Segmentation
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...
Stéphane Nicolas, Thierry Paquet, Laurent H...
ICCV
2007
IEEE
9 years 9 months ago
Steerable Random Fields
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 ...
Stefan Roth, Michael J. Black
CVPR
2008
IEEE
9 years 9 months ago
Learning for stereo vision using the structured support vector machine
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 ...
Yunpeng Li, Daniel P. Huttenlocher
CVPR
2009
IEEE
10 years 2 months ago
Global Connectivity Potentials for Random Field Models
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...
Sebastian Nowozin, Christoph H. Lampert
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