In this paper, we provide an introduction to machine learning tasks that address important
problems in genomic medicine. One of the goals of genomic medicine is to determine how …

Optimization is a powerful paradigm for expressing and solving problems in a wide range of
areas, and has been successfully applied to many vision problems. Discrete optimization …

A general framework for constructing and using probabilistic models of complex systems that
would enable a computer to use available information for making decisions. Most tasks …

The formalism of probabilistic graphical models provides a unifying framework for capturing
complex dependencies among random variables, and building large-scale multivariate …

Convolutional neural networks with many layers have recently been shown to achieve
excellent results on many high-level tasks such as image classification, object detection and …

Many problems in real-world applications involve predicting several random variables that
are statistically related. Markov random fields (MRFs) are a great mathematical tool to …

We study the problem of generating plausible interpretations of a scene from a collection of
line segments automatically extracted from a single indoor image. We show that we can …

State-of-the-art research on MRFs, successful MRF applications, and advanced topics for
future study. This volume demonstrates the power of the Markov random field (MRF) in …

This paper introduces a new rigorous theoretical framework to address discrete MRF-based
optimization in computer vision. Such a framework exploits the powerful technique of Dual …

Deblurring an image has been a long researched problem. This problem is very complex
due to the lack of sufficient information about the blur parameters. Image deblurring is …