We develop a framework for learning generic, expressive image priors that capture the statistics of natural scenes and can be used for a variety of machine vision tasks. The appro...
This paper presents an edge-directed super-resolution algorithm for gray level document images without using any training set. This technique creates an image with smooth regions ...
Belief propagation over pairwise connected Markov Random Fields has become a widely used approach, and has been successfully applied to several important computer vision problems....
The spatial distribution of gray level intensities in an image can be naturally modeled using Markov Random Field (MRF) models. We develop and investigate the performance of face ...
Considerable advances have been made in learning to recognize and localize visual object classes. Simple bag-offeature approaches label each pixel or patch independently. More adv...