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...
Abstract. Imaging is a class of non-Bayesian methods for the revision of probability density functions originally proposed as a semantics for conditional logic. Two of these revisi...
A well-built dataset is a necessary starting point for advanced computer vision research. It plays a crucial role in evaluation and provides a continuous challenge to stateof-the-...
In previous work on "transformed mixtures of Gaussians" and "transformed hidden Markov models", we showed how the EM algorithm in a discrete latent variable mo...
In this paper we introduce a novel contextual fusion method to improve the detection scores of semantic concepts in images and videos. Our method consists of three phases. For eac...