This Computer Vision course provides an introduction to computer vision including fundamentals of image formation, camera imaging geometry, feature detection and matching, multi-view geometry including stereo, motion estimation, and tracking, and classification.
We’ll develop basic methods for applications that include finding known models in images, depth recovery from the stereo, camera calibration, image stabilization, automated alignment (e.g. panoramas), tracking, and action recognition. We focus less on the machine learning aspect of CV as that is really a classification theory best learned in an ML course.
What Will I Learn?
- Learn Image Classification
- Learn Neural Networks
- Learn CNN Architectures
- Learn Detection and Segmentation
- Learn Generative Models