Computer Vision

Computer vision is an exciting and rapidly changing field. In a little over ten years, deep learning algorithms have revolutionized several aspects of computer vison. Applications that were infeasible or impractical a few years ago are now in routine production. These advances allow intelligent systems to interact with the real-world using vision. Examples of modern computer vision (CV) applications include digital photography, robotic vision, autonomous vehicles, medical imaging, and scientific imaging. This course is a fast-moving survey of both fundamental theory of CV algorithms along with hands-on practical assignments applying these methods using Python. Successfully deploying CV applications often requires a combination of classical methods and state-of-the-art algorithms. Therefore, this course covers the classical image processing and CV techniques often found in practical CV solutions. From this foundation the course moves to the deep learning algorithms that have revolutionized CV. Students apply tools drawn from the extensive universe of Python CV related packages in the hands-on assignments to reinforce key principles. Major topics covered in the course include: algorithms used to prepare images, transform images, and extract features; statistical properties of images and methods of decomposition; machine learning algorithms for CV, including deep learning; classification of objects in images; motion in images and optical flow; object detection and tracking algorithms; models for stereo vision; segmentation of images; and generative models.