This course will cover essentials of Computer Vision. We will learn basic principles of image formation, image processing algorithms and different algorithms for 3D reconstruction and recognition from single or multiple images (video). Aplications to 3D modelling, video analysis, video surveillance, image based retrieval object recognition and vision based control will be discussed.  

This course is of interest to anyone seeking to process images or camera information, or to acquire a general background in issues related to real-world perception, image processing, object and scene recognition and multi-view geometry.

Grading Homeworks (about every 2 weeks) 40% Exam: 30% Final project: 30%
Prerequisites linear algebra, calculus
Lecture Materials Lecture slides, lecture notes provided by instructor

Recommended Textbooks

[1] Invitation to 3D Vision: From Images to Geometric Models: Y. Ma, S. Soatto, J. Kosecka and S. Sastry web site
[2] Computer Vision: A Modern Approach: D. Forsythe and J. Ponce, Prentice-Hall, 2003
[3] Image Processing, Analysis, and Machine Vision. Sonka, Hlavac, and Boyle. Thomson.
[4] Computer Vision. Ballard and Brown web site
[5] Computer Vision: Algorithms and Applications. R. Szeliski, 2010, Springer online version of the book

Required Software

Matlab, OpenCV. Homeworks will require using Matlab and OpenCV. You can buy a student version in Johnson center or use it remotely from ITE labs. OpenCV is an C/C++ open source computer vision library.