Course description. This course introduces the fundamental concepts of vision with emphasis on computer science and engineering.  In particular, the course covers the image formation process, image representation, feature extraction, stereopsis, motion analysis, 3D parameter estimation and applications.

Course textbook. Richard Szeliski, Computer Vision: Algorithms and Applications (available for free or purchase)

Lectures.

#	TOPIC	SLIDES	BACKGROUND MATERIAL
0	Introduction to computer vision	PDF MOV	Szeliski - Chapter 1 Eero Simoncelli, A Geometric Review of Linear Algebra
IMAGE FORMATION
1	Image formation (part 1)	PDF MOV	Szeliski - Chapter 2 (Sec. 2.1) History of Photography in 5 Minutes (video)
2	Image formation (part 2)	PDF MOV
3	Image transformations	PDF MOV
4	Image metrology	PDF MOV	Bringing Pictorial Space to Life
IMAGE FILTERING AND FEATURE EXTRACTION
5	Image filtering: Smoothing	PDF MOV	Szeliski - Chapter 3 (Secs. 3.1 to 3.2.2)
6	Image filtering: Edge detection	PDF MOV	Szeliski - Chapter 4 (Sec. 4.2) Pedro Felzenszwalb, Edge Detection Avidan and Shamir, Seam Carving for Content-Aware Image Resizing
7	Image features	PDF MOV	Szeliski - Chapter 4 (Sec. 4.1)
MODEL FITTING
8	Model fitting	PDF MOV	Szeliski - Chapters 4 (Sec. 4.3.2) and 6 (Secs. 6.1.1, 6.1.2 and 6.1.4) Ballard and Brown, Hough transform
FREQUENCY ANALYSIS
9	Frequency analysis: Theory	PDF MOV	Szeliski - Chapter 3 (Sec. 3.4)
10	Frequency analysis: Applications	PDF MOV	Horn - Chapters 6 and 7
MULTIPLE IMAGE ANALYSIS
11	Motion analysis	PDF MOV	Trucco and Verri - Chapter 8 (Secs. 8.3 and 8.4) Fleet and Weiss, Optical Flow Estimation (Secs. 1 and 2)
12	Stereopsis	PDF MOV	Trucco and Verri - Chapter 7
13	3D structure and motion	PDF MOV	Trucco and Verri - Chapter 8 (Secs. 8.1 and 8.2)
14	Tomasi and Kanade Factorization Method	PDF MOV	Tomasi and Kanade, Shape and Motion from Image Streams under Orthography: A Factorization Method
15	Procrustes analysis	PDF MOV
RECOGNITION
16	Machine learning crash course	PDF MOV

Reference textbooks.
     Gilbert Strang, Linear Algebra and Its Applications (video lectures)
     Berthold Horn, Robot Vision, MIT Press.
     Emanuele Trucco and Alessandro Verri, Introductory Techniques for 3-D Computer Vision, Prentice Hall.

Useful links.
     Python Numpy tutorial
     Basic Linear algebra review
     Linear algebra review and reference

Acknowledgements. While a great effort has been made to assemble an original set of lecture slides, the essence of the presentation of many of the slides rely significantly on slides prepared by the following instructors: Richard Wildes, Kostas Daniilidis, James Hays, Derek Hoiem, Aaron Bobick, David Lowe, Kristen Grauman, Robert Collins, Svetlana Lazebnik, Steve Seitz, William Freeman, Robert Pless, Andrej Karpathy and Alyosha Efros.