MAP inference in Discrete Models
published: Oct. 9, 2012, recorded: September 2012, views: 1188
Report a problem or upload filesIf you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Many problems in Computer Vision are formulated in form of a random filed of discrete variables. Examples range from low-level vision such as image segmentation, optical flow and stereo reconstruction, to high-level vision such as object recognition. The goal is typically to infer the most probable values of the random variables, known as Maximum a Posteriori (MAP) estimation. This has been widely studied in several areas of Computer Science (e.g. Computer Vision, Machine Learning, Theory), and the resulting algorithms have greatly helped in obtaining accurate and reliable solutions to many problems. These algorithms are extremely efficient and can find the globally (or strong locally) optimal solutions for an important class of models in polynomial time. Hence, they have led to a significant increase in the use of random field models in computer vision and information engineering in general. This tutorial is aimed at researchers who wish to use and understand these algorithms for solving new problems in computer vision and information engineering. No prior knowledge of probabilistic models or discrete optimization will be assumed. The tutorial will answer the following questions: (a) How to formalize and solve some known vision problems using MAP inference of a random field? (b) What are the different genres of MAP inference algorithms? (c) How do they work? (d) What are the recent developments and open questions in this field?
Link this pageWould you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !