Lecture: Markov Random Fields for Vision and Graphics

Description

This course addresses advanced topics for Markov Random Fields and their use in applications for vision and graphics.  We will cover advanced topics in inference and learning such as loopy belief propagation, MCMC sampling, graph cuts and move-making algorithms, dual decomposition and structured learning.  Applications discussed will include low and mid-level vision and graphics concepts such as optical flow and stereo depth, super-resolution, superpixels, texture synthesis, segmentation as well as higher-level concepts such as semantic segmentation and object detection.

It is recommended but not required to have taken Probabilistic Graphical Models (MA-INF 4315) as a pre-requisite for this course.  Those who have not taken Probabilistic Graphical Models should be comfortable with concepts in probability theory and optimization.