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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: Tim Brooks (Office hours: 1 - 2 PM Monday, 1 - 2 PM Thursday), Vickie Ye (Office hours: 1 - 2 PM Monday, 1 - 2 PM Thursday)
Tutors: Kamyar Salahi (Office hours: 3 - 4 PM Tuesday), Lily Yang (Office hours: 3 - 4 PM Friday), Violet Yao (Office hours: 2 - 3 PM Wednesday)
SEMESTER: Fall 2021
Q&A: Piazza
Gradescope Entry Code: RWBKJJ

Syllabus: here

LOCATION: Lewis Hall 100
: 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

See student submissions here

Class Choice Awards: Norman Karr


Project 2: Fun with Filters and Frequencies


See student submissions here

Class Choice Awards: Alina Dan


Project 3: Face Morphing and Modelling a Photo Collection



See student submissions here


Class Choice Awards: Norman Karr


Project 4: (Auto)stitching and photo mosaics


See student submissions here

Class Choice Awards: Norman Karr


Project 5: Facial Keypoint Detection with Neural Networks


See student submissions here

Kaggle Competition Winner: Matthew Lacayo (Kaggle Username: Anant Sahai)


Final Project


See student submissions pre-canned own-proposed

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 25


Aug 30

Capturing Light... in man and machine

Sep 1

Point Processing & Filtering
Pinhole Camera


Sep 8

Convolution and Image Derivatives

  • Slides: pptx, pdf
  • Start Szeliski Ch 3

Sep 13

The Frequency Domain

  • Slides: pdf, ppt
  • Szeliski Ch 3.4

Sep 15

Pyramid Blending, Templates, NL Filters

Sep 20

Image Transformations

Sep 22

Image Warping and Morphing

Sep 27

Data-driven Methods: Faces

Sep 29

The Camera

Slides: pdf, ppt

Oct 04

The Camera

Slides: pdf, ppt

Oct 06

Homographies and Mosaics

  • Slides: pdf, ppt
  • Szeliski Ch 8

Oct 11

More Mosaic Madness

Oct 13

Automatic Image Alignment

Oct 18

Automatic Image Alignment + Optical Flow

Oct 20

Visual Texture (in human and machine)

Slides: pdf, ppt

Oct 25

Feature Learning with Neural Networks

Slides: pdf, ppt

Oct 27

Convolutional Neural Networks

Slides: pdf, ppt

Nov 1

Convolutional Neural Networks II

Nov 3

ConvNets as a Versatile Tool

Nov 8

3D Vision: Calibration, Stereo

Nov 8

3D Vision: Epipolar Geometry

Nov 15

3Structure-from-Motion (SfM) and Multi-View Stereo (MVS)

Nov 22

Video & Texture Synthesis

Nov 29

Modeling the Plenoptic Function

Dec 1

What Makes a Great Picture?

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 (11/17 Wednesday) + potentially some Pop Quizzes (20%), and a final project due on 12/10 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