School of Computing · ANU College of Systems and Society
Computer Vision is an important field of Artificial Intelligence concerned with automatically extracting useful information from images or videos. The explosive growth in digital imaging technologies, vision datasets and deep learning makes the problems of automated image interpretation even more exciting and more relevant than ever. This course introduces students to fundamental problems in computer vision and foundational techniques for solving them. Topics covered in detail include: image formation, image filtering, camera geometry, thresholding and image segmentation, edge, point and feature detection, geometric frameworks for vision, single view and two views geometry, 3D visual reconstruction, camera calibration, stereo vision, image classification, object recognition, deep learning and neural networks. The course features extensive practical components including computer labs that provide students with the opportunity to practice and refine their skills in computer vision.
Assignment 1
Learning outcomes 1, 2, 5, 6
Assignment 2
Learning outcomes 1, 2, 3, 5
Assignment 3
Learning outcomes 1, 2, 5, 6
Final Exam
Learning outcomes 1, 2, 3, 4, 5
130 hours consisting of lectures, tutorials, labs, and self-directed study.
No prescribed textbook is needed. Preliminary Reading Computer Vision: Algorithms and Applications - Szeliski.org szeliski.org/Book/