|Pattern theory in imaging science|
|Instructor：||Ren Guo [University of Minnesota]|
|Place：||Conference Room 1, Floor 1, Jin Chun Yuan West Building|
Pattern theory provides a framework for describing and analyzing all patterns in the world including images, sounds, written text or genetic code. It requires models using mathematics in different fields, for example, stochastic analysis and differential geometry. Pattern theory has broad applications in biomedical imaging, computer vision, computational linguistics, speech recognition and face recognition.
This course is an introduction to pattern theory and focuses on pattern theory in imaging science. It covers some topics in mathematical foundation of pattern theory including Gibbs random fields, Gaussian random fields, geometric deformable templates, computational anatomy.
This course should be appropriate for graduate students as well as advanced undergraduate students in applied mathematics, computer science, electrical engineering and biomedical engineering.
Elementary differential geometry, Elementary probability
Reference for the course:
 Ulf Grenander, Michael Miller, Pattern Theory: From Representation to Inference, Oxford University Press, 2007
 David Mumford, Agnès Desolneux, Pattern Theory: The Stochastic Analysis of Real-World Signals, A K Peters, Ltd., 2010