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COMPSCI280A

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COMPSCI 280A - Intro to Computer Vision and Computational Photography

Electrical Engineering and Computer Sciences Graduate COE - College of Engineering

Subject

COMPSCI

Course Number

280A

Department

Electrical Engineering and Computer Sciences

Course Level

Graduate

Course Title

Intro to Computer Vision and Computational Photography

Course 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.

Minimum Units

4

Maximum Units

4

Grading Basis

Default Letter Grade; S/U Option

Instructors

Efros, Kanazawa

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

Repeat Rules

Course is not repeatable for credit.

Credit Replacement Courses

-

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.

Formats

Discussion, Lecture

Term

Fall and Spring

Weeks

15 weeks

Weeks

15

Lecture Hours

3

Lecture Hours Min

3

Lecture Hours Max

3

Lecture Mode of Instruction

In Person

Discussion Hours

1

Discussion Hours Min

1

Discussion Hours Max

1

Discussion Mode of Instruction

In Person

Outside Work Hours

10

Outside Work Hours Min

10

Outside Work Hours Max

10