The course will take an indepth look at various Bayesian methods in computer and medical vision. Through the language of Bayesian inference, the course will present a coherent view of the approaches to various key problems such as detecting objects in images, segmenting object boundaries, and recognizing objects. The course is roughly partitioned into two halves: modeling and inference. In the first half, we will cover both classical models such as weak membrance models and markov random fields as well as more recent models such as conditional random fields, latent dirichlet allocation, and topic models. In the second half, we will focus on inference algorithms. Methods will range from PDE boundary evolution algorithms such as region competition, and discrete optimization methods such as graph-cuts and graph-shifts, to stochastic optimization methods such as data-driven markov chain monte carlo. An emphasis will be placed on both the theoretical aspects of this field as well as the practical application of the models and inference algorithms.
Each student will be required to implement a course project that is either a direct implementation of a method discussed during the semester or new research in Bayesian vision. A completed paper describing the project is required at the end of the semester (8 pages two column IEEE format) and we will have an open-house poster session to present the projects. Working project demos are suggested but not required for the poster session.
It is assumed that the students have taken introductory courses in, pattern recognition (CSE 555), and computer vision (CSE 573). Permission of the instructor is required if these pre-requisites have not been met.
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