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CS194-26/294-26: Intro to Computer Vision and Computational Photography
Computer Science Division
University of California Berkeley

INSTRUCTOR: Alexei (Alyosha) Efros (Office hours: after lecture), Angjoo Kanazawa (Office hours: after lecture)
GSI: Evonne Ng (Office hours: Tue. 2PM - 3PM & Thu. 11AM - 12PM), Ruilong Li (Office hours: Tue. 2PM - 3PM & Thu. 11AM - 12PM)
Tutors: Kamyar Salahi (Office hours: Wed. 1PM - 2PM), Jerry Ma (Office hours: Mon. 2PM - 3PM), Jason Ding (Office hours: Wed. 3PM - 4PM), Jeffrey Shen (Office hours: Thu. 1PM - 2PM)
SEMESTER: Fall 2022
Google Calender: (Public URL)
Gradescope Entry Code: E7JNYB

Syllabus: here

LOCATION: Dwinelle 145
: MW 5:00 PM-6:30 PM

This is a heavily project-oriented class, therefore good programming proficiency (at least CS61B) is absolutely essential. Moreover, familiarity with linear algebra (MATH 54 or EE16A/B or Gilbert Strang's online class) and calculus are vital. Experience with neural networks (e.g. CS182 or equivalent) is strongly recommended. Due to the open-endedness of this course, creativity is a class requirement.

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.


Project 1: Images of the Russian Empire -- Colorizing the Prokudin-Gorskii Photo Collection

Class Choice Awards: Janise Liang
Runner ups: Shufan Li  Anik Gupta  Bryan Ngo 


Project 2: Fun with Filters and Frequencies


Class Choice Awards: Skylar Sarabia
Runner ups: Anik Gupta  Erich Liang  Andrew Zhang 


Project 3: Face Morphing and Modelling a Photo Collection


Class Choice Awards: Joshua Chen
Runner ups: Zixun Huang  Skylar Sarabia  Jerome Quenum 


Project 4: (Auto)stitching and photo mosaics



Project 5: Facial Keypoint Detection with Neural Networks



Final Project


We will be loosely using the new 2nd edition of Rick Szeliski's Computer Vision textbook. The latest draft is available off the textbook's website. If you find a bug or a typo, please e-mail Rick for a chance to get an acknowledgement in the finished book! The first edition is still available at the bookstore, but it's missing some important things, like discussion of Convolutional Neural Networks.

There is a number of other fine texts that you can use for general reference:

Computer Vision: A Modern Approach (2nd edition), Forsyth and Ponce (classic computer vision text)
Vision Science: Photons to Phenomenology, Stephen Palmer (great book on human visual perception)
Digital Image Processing, 2nd edition, Gonzalez and Woods (a good general image processing text)
Linear Algebra and its Applications, Gilbert Strang (a truly wonderful book on linear algebra)

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.






Aug 24


Aug 29

Capturing Light... in man and machine

Sep 1

Point Processing & Filtering
Pinhole Camera

  • Slides: ppt, pdf
  • Szeliski Ch. 2


Sep 7

Convolution and Image Derivatives

  • Slides: pptx, pdf
  • Start Szeliski Ch 3

Sep 12

The Frequency Domain

Sep 14

Pyramid Blending, Templates, NL Filters

Sep 19

Image Transformations

Sep 21

Image Warping and Morphing

Sep 26

Data-driven Methods: Faces

Sep 28

The Camera

Oct 03

Homographies and Mosaics

Oct 5

Automatic Image Alignment

Oct 10

Automatic Image Alignment + Optical Flow

Oct 12

Visual Texture (in human and machine)

Oct 17

Feature Learning with Neural Networks

Oct 19

Convolutional Neural Networks

Oct 24

Convolutional Neural Networks II

Oct 26

Sequence Models for words and pixels

Oct 31

Generative Models

Nov 02

3D Vision: Calibration, Stereo

Nov 07

3D Vision: Calibration, Stereo

Nov 09

3D Vision: Calibration, Stereo

Nov 14

Multi-Perspective Panoramas

Nov 21

What Makes a Great Picture?

Nov 30

Neural Radiance Fields 1

Dec 07

Neural Radiance Fields 2

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

Grading will be based on a set of programming and written assignments (60%), a midterm exam (11/16 Wed.) + potentially some Pop Quizzes (20%), and a final project due on 12/09 Friday (20%).  For the programming assignments, students will be allowed a total of 5 (five) late days per semester; each additional late day will incur a 10% penalty.

Students taking CS294-26 will also be required to submit a conference-style paper describing their final project.

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 build-in image processing functions.  Here is a link to some useful MATLAB and Python resources compiled for this class.

Previous offerings of this course can be found here.



Page design courtesy of Doug James