Spring 2022

Carnegie Mellon University

This course gives an expansive introduction to computer vision, focusing on image processing, recognition, geometry-based and physics-based vision, and video analysis. Students will gain practical experience solving real-life vision problems. It requires a good understanding of linear algebra, calculus, and programming.

This course provides a comprehensive introduction to computer vision. Major topics include image processing, detection and recognition, geometry-based and physics-based vision and video analysis. Students will learn basic concepts of computer vision as well as hands on experience to solve real-life vision problems.

This course requires familiarity with linear algebra, calculus, basic probability, as well as programming. In particular, the following courses serve as prerequisite:

- "Mathematical Foundations of Electrical Engineering" (18-202) and "Principles of Imperative Computation" (15-122)

OR

- "Matrix Algebra with Applications" (21-240) and "Matrices and Linear Transformations" (21-241) and "Calculus in Three Dimensions" (21-259) and "Principles of Imperative Computation" (15-122)

No data.

Readings will be assigned from the following textbook (available online for free):

**Computer Vision**: Algorithms and Applications, by Richard Szeliski. Additional readings will be assigned from relevant papers. Readings will be posted on the website.

The following textbooks can also be useful references for different parts of the class, but are not required:

- Multiple View Geometry in Computer Vision, by Richard Hartley and Andrew Zisserman.
- Computer Vision: A Modern Approach, by David Forsyth and Jean Ponce.
- Digital Image Processing, by Rafael Gonzalez and Richard Woods.

Lecture slides available at Lectures

No videos available

Assignments available at Assignments

Quizzes available at Quizzes

Readings available at Lectures

Notebooks and interactive demos available at Notebooks and Interactive Demos

2D TransformationsAlignment and TrackingConvolutional neural network (CNN)Detecting CornersDigital PhotographyFeature Detectors and DescriptorsFrequency DomainGeometric Camera ModelsHough TransformImage ClassificationImage FilteringImage HomographiesImage PyramidsNeural networkOptical FlowPhotometric StereoRadiometry and ReflectanceStereoTwo-View Geometry