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 ...
This paper describes how a visual system can automatically define features of interest from the observation of a large enough number of natural images. The principle complements t...
Unsupervised segmentation of weather images into features that correspond to physical storms is a fundamental and difficult problem. Treating an infrared satellite image as a Mark...
We address the normal reconstruction problem by photometric stereo using a uniform and dense set of photometric images captured at fixed viewpoint. Our method is robust to spurio...
We propose an algorithm for the binarization of document images degraded by uneven light distribution, based on the Markov Random Field modeling with Maximum A Posteriori probabil...