CS180/280A

 

Description: [SCS dragon logo]

CS180/280A: Intro to Computer Vision and Computational Photography
Computer Science Division
University of California Berkeley

Instructor: Alexei (Alyosha) Efros (Office hours: After Lecture),
GSI: Hang Gao (DIS: Thur. 10AM - 11AM, OH: Thur. 2PM - 3PM), Vongani Maluleke (DIS: Wed. 11AM - 12PM, OH: Mon. 12PM - 1PM)
Tutors: Bill Zheng (OH: Wed. 9AM - 10AM), Daniel George (OH: Fri. 6PM - 7PM), Max Vogel (OH: Fri. 11AM - 12PM), Ryan Tabrizi (OH: Tue. 2PM - 3PM) 
Reader: Xiaowen Yuan  
University Units: 4
Semester: Fall 2024
Web Page: CS180/280A FA24 Web Page
Google Calendar: CS180/280A FA24 Calendar
Ed: CS180/280A FA24 Ed
Gradescope Entry Code: KZNDRJ
Syllabus: Here
Location: Li Ka Shing 245
Time: MW 5PM-6:30PM

PREREQUISITES:
This is a heavily project-oriented class, therefore good programming proficiency (at least CS 61B) is absolutely essential and required. Moreover, working knowledge of linear algebra (MATH 54, MATH 56, MATH 110, or EECS 16A) and multivariate calculus (e.g. MATH 53) are vital. Experience with machine learning and neural networks is required in the second part of the course. You must have been taken beforehand or are currently taking (CS 182 or CS 189). Due to the open-endedness of this course, creativity is a class requirement.

COURSE DESCRIPTION:
The aim of this advanced undergraduate course is to introduce 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 (e.g. Gaussian and Laplacian Pyramids) to contemporary (e.g. ConvNets, GANs), with an emphasis on using these techniques to build practical systems. This hands-on emphasis will be reflected in the programming assignments, in which students will have the opportunity to acquire their own images and develop, largely from scratch, the image analysis and synthesis tools for solving applications.

PROGRAMMING ASSIGNMENTS:
Project 1: Images of the Russian Empire -- Colorizing the Prokudin-Gorskii Photo Collection
Description: http://www.cs.cmu.edu/afs/andrew/scs/cs/15-463/pub/www/images/3-8086-left.jpg
See student submissions here.
Class Choice Award: Haoyue Xiao
Runner ups: Adrian Kwan, Clara Hung
Project 2: Fun with Filters and Frequencies
hybrid orple
See student submissions here.
Class Choice Award: Nicolas Rault-Wang
Runner ups: Aditya Tummala, Kaitlyn Chen

Project 3: Face Morphing and Modelling a Photo Collection
morph
See student submissions here.

Project 4: (Auto)stitching and photo mosaics
stitching
See student submissions here.

Project 5: Fun with Diffusion Models
diffusion
See student submissions here.

Final Project
final


See the project submission specification.

TEXTBOOK:

CLASS NOTES
The instructor is extremely grateful to a large number of researchers for making their slides available for use in this course. Steve Seitz and Rick Szeliski have been particularly kind in letting me use their wonderful lecture notes. In addition, I would like to thank Paul Debevec, Stephen Palmer, Paul Heckbert, David Forsyth, Steve Marschner and others, as noted in the slides. The instructor gladly gives permission to use and modify any of the slides for academic and research purposes. However, please do also acknowledge the original sources where appropriate.

   

CLASS SCHEDULE:

CLASS DATE TOPICS MATERIAL
Aug 28 Introduction
intro
Sep 04 Capturing Light... in man and machine
capturing-light
Sep 09 Image Processing I
point-processing
Sep 11 Image Processing II: Convolution and Derivatives
convolution
Sep 16 The Frequency Domain
fourier
Sep 18 Pyramid Blending, Templates, NL Filters
pyramids-blending
Sep 23 Image Transformations
image-transformation
Sep 25 Image Warping and Morphing
morphing
Sep 30 Data-driven Methods: Faces
faces
Oct 07 The Camera
the-camera
  • Slides: pptx, pdf
  • Perspective projection in 5 minutes: Video
Oct 09 Homographies and Mosaics
mosaic
Oct 14 Automatic Image Alignment Part 1
Automatic Image Alignment Part 1
Oct 16 Automatic Image Alignment Part 2
Automatic Image Alignment Part 2
Oct 21 Automatic Image Alignment Part 3
Automatic Image Alignment Part 3
Oct 23 Texture Models
Texture Models
Oct 28 Image-to-Image Translation
Image-to-Image Translation
Oct 31 Generative Models of Images
Generative Models of Images
Nov 04 Diffusion Models
Diffusion Models
Nov 06 Sequence Models for words and pixels
Sequence Models for words and pixels
Nov 13 3D Modeling for a Single View
Sequence Models for words and pixels
Nov 18 Stereopsis and Epipolar Geometry
Stereopsis and Epipolar Geometry
Nov 20 Structure-from-Motion (SfM)
Structure-from-Motion (SfM)
Nov 25 Multi-View Stereo, Neural Radiance Fields,and Data-driven 3D
Multi-View Stereo, Neural Radiance Fields,and Data-driven 3D
Dec 02 Photography, Art, and AI
Multi-View Stereo, Neural Radiance Fields,and Data-driven 3D

CAMERAS:
Although it is not required, students are highly encouraged to obtain a digital camera for use in the course.

GRADING:

Students will be allotted a total of 5 (five) late days per semester with each additional late day incurring a 10% penalty. Students taking CS280A will also be required to submit a conference-style paper describing their final project. There will be opportunities to complete quiz-drop “cookies” on projects. For every cookie you complete, 1 quiz can be dropped from your average. You can earn up to 2 cookies, and only integer-amount of cookies can be redeemed (no fractions). Students taking CS280A will also be required to submit a conference-style paper describing their final project.

PROGRAMMING RESOURCES:
Students will be encouraged to use either Python (with either scikit-image or opencv) or MATLAB (with the Image Processing Toolkit) as their primary computing platform. Specific libraries in both languages offer tons of built-in image processing functions. Here is a link to some useful MATLAB and Python resources compiled for this class.

PREVIOUS OFFERINGS OF THIS COURSE:
Previous offerings of this course can be found here.

SIMILAR COURSES IN OTHER UNIVERSITIES:

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