1. Computer Vision

This course deals with the science and engineering of computer vision, that is, the analysis of patterns in visual images of a 3D scene with the goal of interpreting, understanding, and reconstructing the 3D scene. The emphasis is on physical, mathematical, geometric and information processing aspects of vision. Topics to be covered include image formation and representation, feature extraction, camera calibration, image noise representation and propagation, stereo vision, projective geometry, 3D reconstruction, structure from motion, tracking, and analytical performance characterization. In addition, the course will cover applications of computer vision techniques for face detection and recognition, facial feature tracking, eye tracking, facial expression understanding, and medical image segmentatation. This course will be very useful for students interested in human computer interaction, robotics, photogrammetry, remote sensing, and medical imaging.

2. Pattern Recognition

This course introduces fundamental concepts, theories, and algorithms for pattern recognition and machine learning. Topics to be covered include linear regression, linear classification, support vector machines, dimensionality reduction, clustering, boosting, and probabilistic graphical models.

3. Probabilistic Graphical Models

Probabilistic Graphical Models (PGMs) are an indispensable tool to machine learning, with applications in many different fields. As a marriage between probability theory and graph theory, PGMs provide a tool for dealing with two problems that occur throughout applied mathematics and engineering uncertainty and complexity. Under probabilistic models, data are modelled as a collection of random variables with a particular pattern of possible dependencies between them. Using the model, we can then discover knowledge, predict future events, and infer hidden causes. This 3-credit graduate-level course will introduce theories and applications for various PGMs including Bayesian Networks, Markov Random Fields, Conditional Random Fields, and Hidden Markov Models. Theoretically, we will study various model learning and inference methods. Application-wise, we will demonstrate the use of graphical models for different applications including computer vision, human computer interaction, natural language processing, data mining, and bioinformatics. Through this course, students will understand the basic theories underlying different application models. In addition, it will provide students with a strong foundation for both applying graphical models to complex problems in their own research areas and for addressing core research topics in graphical models.

4. Mathematical Techniques for Computer Vision, Graphics and Robotics

This course is taught by Prof. Chuck Stewart. The goal of this course is to provide an introduction to some of the mathematical background needed to do research in computer vision, computer graphics and robotics.

5. Statistical and Learning Techniques for Computer Vision