This is the sample implementation of a Markov random field based image segmentation algorithm described in the following papers:
1. Mark Berthod, Zoltan Kato, Shan Yu, and Josi...
This is the sample implementation of a Markov random field based color image segmentation algorithm described in the following paper:
Zoltan Kato, Ting Chuen Pong, and John Chu...
We introduce a novel data-driven mean-shift belief propagation
(DDMSBP) method for non-Gaussian MRFs, which
often arise in computer vision applications. With the aid
of scale sp...
We present a novel multi-view stereo method designed
for image-based rendering that generates piecewise planar
depth maps from an unordered collection of photographs.
First a di...
Sudipta N. Sinha, Drew Steedly and Richard Szelisk...
Partially occluded faces are common in many applications
of face recognition. While algorithms based on sparse
representation have demonstrated promising results, they
achieve t...
Zihan Zhou, Andrew Wagner, Hossein Mobahi, John Wr...
In this paper, we introduce a higher-order MRF optimization
framework. On the one hand, it is very general;
we thus use it to derive a generic optimizer that can be applied
to a...
Nikos Komodakis (University of Crete), Nikos Parag...
Convex and continuous energy formulations for low level
vision problems enable efficient search procedures for the
corresponding globally optimal solutions. In this work we
exte...
Christopher Zach (UNC Chapel Hill), Marc Niethamme...
Many computer vision problems can be formulated in
a Bayesian framework with Markov Random Field (MRF)
or Conditional Random Field (CRF) priors. Usually, the
model assumes that ...
Much research effort on Automatic Image Annotation
(AIA) has been focused on Generative Model, due to its
well formed theory and competitive performance as compared
with many we...
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...