Computer Vision: Algorithms and Applications, 2nd ed.

© 2022 Richard Szeliski, The University of Washington

Welcome to the website (https://szeliski.org/Book) for the second edition of my computer vision textbook, which is now available for purchase at Amazon, Springer, and other booksellers.

To download an electronic version of the book, please fill in your information on this page. You are welcome to download the PDF website for personal use, but not to repost it on any other website; please post a link to this URL instead.

Note that while the content of this electronic version and the hardcopy versions are the same, the page layout is different, since the electronic version is optimized for online reading. The PDF should be enabled for commenting in your viewer. Also, hyper-links to sections, equations, and references are enabled. To get back to where you were, use the Previous View (Alt-Left-Arrow) command in Acrobat.

The current download count is 91521 (since 1/23/2022).

This book is largely based on the computer vision courses that I have co-taught at the University of Washington (2020, 2008, 2005, 2001) with Steve Seitz and Harpreet Sawhney and at Stanford (2003) with David Fleet.

If you're curious about the process that went into writing my book, I did an interview with Computer Vision News (March 2022).

First edition

You can still download the first edition or potentially purchase it online. The first edition is also available in Chinese and Japanese (translated by Prof. Toru Tamaki ).

Errata

If you have any comments or feedback on the book, please send me e-mail. Once I have accumulated enough suggestions, I will post an updated draft with the corrections/suggestions as PDF comments.

Slide sets and lectures

There are no official slide sets to go with the book, but please feel free to look at the University of Washington CSE 576 (Graduate Computer Vision) (2020 and 2008 versions) slides that Steve Seitz and I have put together.

Additional good sources for related courses (sorted roughly by most recent first) include:

Noah Snavely's CS5670 - Introduction to Computer Vision class at Cornell Tech (Spring 2023)
Bill Freeman, Antonio Torralba, and Phillip Isola's 6.8300/6.8301: Advances in Computer Vision class at MIT (Spring 2023)
Yasutaka Furukawa's CMPT 412 - Computer Vision class at Simon Fraser University (Spring 2023)
David Fouhey's EECS 442: Computer Vision class at the University of Michigan (Winter 2023)
Alyosha Efros' CS194-26/294-26: Intro to Computer Vision and Computational Photography class at Berkeley (Fall 2022)
James Hays' CS 4476-A / 6476-A Computer Vision class at Georgia Tech (Fall 2022)
James Tompkin's CSCI 1430 Computer Vision class at Brown (Spring 2023)
Ioannis Gkioulekas's 15-463, 15-663, 15-862 Computational Photography class at CMU (Fall 2023)
Matthew O'Toole's 16-385 Computer Vision class at CMU (Fall 2022)
Justin Johnson's EECS 498.008 / 598.008: Deep Learning for Computer Vision class at the University of Michigan (Winter 2022), which is an outstanding introduction to deep learning and visual recognition
Yann LeCun and Alfredo Canziani's DS-GA 1008: Deep Learning class at NYU (Spring 2021)
Luiz Velho's Fundamentals and Trends in Vision and Image Processing class at IMPA (Spring 2021)
UC Berkeley's CS294-158-SP20: Deep Unsupervised Learning class (Spring 2020)
Scott Wehrwein's CSCI 497P/597P - Introduction to Computer Vision class at Western Washington University (Spring 2020)
Andrew Owens' EECS 504: Foundations of Computer Vision class at the University of Michigan (Winter 2020)

If you would like your course listed here, please contact me.

Last updated 8/21/2023