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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)
UNIVERSITY UNITS: 4
SEMESTER: Fall 2021
WEB PAGE: http://inst.eecs.berkeley.edu/~cs194-26/fa21/
Q&A: Piazza
Gradescope Entry Code: RWBKJJ

Syllabus: here

LOCATION: Lewis Hall 100
TIME
: MW 5:00 PM-6:30 PM

PREREQUISITES:
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.

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 Awards:

 

Project 2: Fun with Filters and Frequencies

orple


See student submissions here

Class Choice Awards:

 

Project 3: Face Morphing and Modelling a Photo Collection

morph

 

See student submissions here

 

Class Choice Awards:

  

Project 4: TBD

morph


Class Choice Awards:

 

Project 5: (Auto)stitching and photo mosaics

stitching


Class Choice Awards:

 

Final Project

multifredo

TEXTBOOK:
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)

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.

   

TENTATIVE CLASS SCHEDULE:

CLASS DATE

TOPICS

Material

Aug 25

Introduction

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

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

METHOD OF EVALUATION:
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.

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 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:
Previous offerings of this course can be found here.

SIMILAR COURSES IN OTHER UNIVERSITIES:

 

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