ECE 5554 / ECE 4554: Computer Vision Fall 2017
Textbook and optional references
Lectures are not based on any particular textbook. Our primary reference for this course is:
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Computer Vision: Algorithms and Applications, Richard Szeliski, 2010 The electronic copy of the book in PDF is freely available on the web page: http:szeliski.org/Book |
Other usful references:
Computer Vision: A Modern Approach (2nd edition) by David Forsyth and Jean Ponces
Multiple View Geometry in Computer Vision, Hartley & Zisserman (a bible on recovering 3D geometry)
Concise Computer Vision: An Introduction into Theory and Algorithms by Reinhard Klette
Computer Vision, Shapiro and Stockman (a nice introduction to computer vision)
Linear Algebra and its Applications, Gilbert Strang (excellent book on linear algebra)
Vision Science: Photons to Phenomenology, Stephen Palmer (a great book on human visual perception)
Digital Image Processing, 2nd edition, Gonzalez and Woods (a good general image processing text)
MATLAB
General computer vision resources
Courses
Similar courses in other univiersities
Computational Vision (Derek Hoiem, UIUC)
Honors Machine Learning and Vision (Kristen Grauman, UT Austin)
Computer Vision (Steve Seitz & Rick Szeliski, UWashington)
Computational Vision (James Hays, Georgia Tech)
Related courses in Virginia Tech
Introductory courses:
Computer Vision (Devi Parikh, Virginia Tech)
Introduction to Machine Learning (Dhruv Batra, Virginia Tech)
Introduction to Artificial Intelligence (Bert Huang, Virginia Tech)
Advanced courses:
Deep Learning for Perception(Dhruv Batra, Virginia Tech)
Probabilistic Graphical Models and Large-Scale Learning (Dhruv Batra, Virginia Tech)
Advanced Computer Vision (Devi Parikh, Virginia Tech)
Fundamentals:
ECE 5734 Convex Optimization
STAT 5444 Bayesian Statistics
STAT 4714 Prob and Stat for EE