The goal of computer vision is to compute properties of the three-dimensional world from digital images. Problems in this field include reconstructing the 3D shape of an environment, determining how things are moving, and recognizing people and objects and their activities, all through analysis of images and videos. This course will provide an introduction to computer vision, with topics including image formation, feature detection, motion estimation, image mosaics, 3D shape reconstruction, object/face detection and recognition, and deep learning. Applications of these techniques include building 3D maps, creating virtual characters, organizing photo and video databases, human computer interaction, video surveillance, automatic vehicle navigation, robotics, virtual and augmented reality, medical imaging, and mobile computer vision. This is a project-based course, in which you will implement several computer vision algorithms throughout the semester.
Prerequisites This course will be self-contained; students do not need to have computer vision background. However, the following are required:
Textbook
This course will have readings
from Computer Vision: Algorithms
and Applications (online),
by Richard
Szeliski.
This class uses Piazza for discussions and announcements. Grades will be posted on CMS.
Projects are to be
done either individually or in groups of two, as specified in the project description. |
