Many problems in computer vision can be modeled using
conditional Markov random fields (CRF). Since finding
the maximum a posteriori (MAP) solution in such models
is NP-hard, mu...
Stephen Gould (Stanford University), Fernando Amat...
The Conditional Random Fields (CRF) model, using
patch-based classification bound with context information,
has recently been widely adopted for image segmentation/
labeling. In...
In this paper, we present a novel algorithm for partial
intrinsic symmetry detection in 3D geometry. Unlike previous
work, our algorithm is based on a conceptually simple
and st...
Ruxandra Lasowski, Art Tevs, Hans-Peter Seidel, Mi...
?Gibbsian fields or Markov random fields are widely used in Bayesian image analysis, but learning Gibbs models is computationally expensive. The computational complexity is pronoun...
We present an algorithm to reduce per-pixel search ranges for Markov Random Fields-based stereo algorithms. Our algorithm is based on the intuitions that reliably matched pixels ne...