Catalog Description: This course introduces students to computing with visual data (images and video). We will cover acquisition, representation, and manipulation of visual information from digital photographs (image processing), image analysis and visual understanding (computer vision), and image synthesis (computational photography). Key algorithms will be presented, ranging from classical to contemporary, with an emphasis on using these techniques to build practical systems. The hands-on emphasis will be reflected in the programming assignments, where students will acquire their own images and develop, largely from scratch, image analysis and synthesis tools for real-world applications.

Units: 4

Also Offered As: COMPSCI 280A

Course Objectives: Students will learn classic algorithms in image manipulation with Gaussian and Laplacian Pyramids, understand the hierarchy of image transformations including homographies, and how to warp an image with these transformations., Students will learn how to apply Convolutional Neural Networks for computer vision problems and how they can be used for image manipulation. mechanics of a pin-hole camera, representation of images as pixels, physics of light and the process of image formation, to manipulating the visual information using signal processing techniques in the spatial and frequency domains. Students will learn the fundamentals of 3D vision: stereo, multi-view geometry, camera calibration, structure-frommotion, multi-view stereo, and the plenoptic function

Student Learning Outcomes: After this class, students will be comfortable implementing, from scratch, these algorithms in modern programming languages and deep learning libraries.

Prerequisites: CS61B, enough programming experience to debug complicated programs without much help. CS70 Math 53 (or another vector calculus course) Math 54, Math 110, or equivalent like EECS16A Strongly recommended: CS182

Formats:
Fall: 3.0 hours of lecture and 1.0 hours of discussion per week
Spring: 3.0 hours of lecture and 1.0 hours of discussion per week

Grading Basis: Student Option

Final Exam Status: Yes

---

Links:

• EECS Course Archives (CalNet authentication required)
• CS enrollment policies
• EE enrollment policies
• HKN course maps