Pattern Recognition for Machine Vision
MIT OpenCourseWare offers this advanced course on applying pattern recognition techniques to machine vision problems. Topics include image formation and processing, feature extraction from images, biological object recognition, Bayesian decision theory, and clustering methods. The course frames machine vision as a pattern classification problem and works through the mathematical tools used to solve it, alongside comparisons to how biological systems recognize objects. Materials come from MIT's Brain and Cognitive Sciences department and follow the standard OCW format of lecture notes and assignments, free to access with no certificate offered. Suited to students with some background in linear algebra, probability, and signal processing who want a grounding in the theory behind computer vision and classification systems.