G80.3122-001 -- Fall, 2009

Representation and Analysis of Visual Images

Instructor: Eero Simoncelli
Time: Tue 10-12 am
Location: Meyer Hall, rm 1024
[Take elevator to 10th floor. Phone the "Laboratory for Computational Vision" for access]
Prerequisites: linear algebra, linear systems theory, basic probability/statistics. Some matlab programming experience.

Brief Description: A graduate-level lecture course on theory and tools for representing, manipulating and analyzing visual images. Topics to include: imaging and optics, multi-scale and differential image decompositions, alignment and displacement estimation, pattern matching, statistical image modeling and its use in compression, estimation, enhancement and synthesis. Course includes extensive Matlab exercises.

Date Topic Handouts/Reading Homework
8 Sep
10-12am		 Intro / the visual (plenoptic) world / imaging
[Lecture slides] 		Course description, Background poll,
Plenoptic chapter
  
15 Sep
10-12pm		 Color representation, Multi-D linear systems
[Lecture slides]		  
22 Sep
10-12pm		 Sampling, aliasing    
29 Sep
10-12pm		 Differentiation of sampled images Short article on discrete differentiation  
6 Oct
10-12pm		 Orientation estimation, orientation tensors
[Lecture slides (last 3 sessions)] 		hw1, due 10/16
shift.m, cconv2.m
rconv2.m, localOri.m
13 Oct
10-12pm		 Orientation estimation, steerability
20 Oct
10-12pm		 Matching: fundamentals for displacement estimation or pattern matching
27 Oct
10-12pm		 Coarse-to-fine differential matching Book chapter
3 Nov
10-12pm		 Multi-scale representation

Videos, along with Umesh's notes, are available for most lectures here (local access only).

Auxilliary Reading: Image Processing, Computer Vision, Biological Vision

  • Two-dimensional Imaging. Ronald N. Bracewell. Prentice Hall, 1995.
  • Pattern Classification, Duda, Hart and Storck. Wiley, 2001.
  • Computer vision: A modern approach. David Forsyth and Jean Ponce. Prentice Hall, 2003.
  • Digital Image Processing . Bernd Jähne. Springer, 1997.
  • A Wavelet Tour of Signal Processing. Stephane Mallat. Academic Press, 1998.
  • Foundations of Vision. Brian Wandell. Sinauer, 1995.

    Background Material (mathematics/engineering)

  • My linear algebra notes: linearAlgebra, least squares estimation.
  • Linear algebra appendix from the PDP series, by Michael Jordan: linearAlgebra
  • My convolution/Fourier notes: linSys
  • Linear Algebra and Its Applications. Gilbert Strang. Academic Press, 1980.
  • Discrete-Time Signal Processing. Alan Oppenheim and Ronald Schafer. Prentice-Hall, 1989.
  • Elements of Information Theory. Thomas Cover and Joy Thomas. Wiley Series in Telecommunication, 1991.
  • Probability, Random Variables, and Stochastic Processes. A. Papoulis. 3rd edition, McGraw-Hill, 1991.
  • Probability and Statistics, Morris DeGroot and Mark Schervish. Addison-Wesley, 2002.