Computer Vision

This course introduces the theoretical and practical aspects of computer vision, covering both classical and state of the art deep-learning based approaches. This course covers everything from the basics of the image formation process in digital cameras and biological systems, through a mathematical and practical treatment of basic image processing, space/frequency representations, classical computer vision techniques for making 3-D measurements from images, and modern deep-learning based techniques for image classification and recognition.

Student Learning Outcomes

• Be able to read and understand research papers in the computer-vision literature.

• Build computer vision systems to solve real-world problems.

• Properly formulate problems with the appropriate mathematical and computational tools.

• Understand the building blocks of classical computer vision techniques.

• Understand the building blocks of modern computer vision techniques (primarily artificial neural networks).

• Understand the process by which images are formed and represented.

Course Designers

Profile profile for hfarid

Hany Farid

Professor (I School and EECS)

hfarid@ischool.berkeley.edu

203A South Hall

(510) 664-4438

Website

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Shruti Agarwal

Lecturer

shruti_agarwal@ischool.berkeley.edu