G80.3122-001 / G63.2840.003 -- Fall, 2003

Representation and Analysis of Visual Images

Instructor: Eero Simoncelli
Time: Wed 10-12 am (Occasional: Thur 4-6pm, shown in BLUE)
Location: Meyer Hall, rm 1024
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, statistical image modeling and its use in compression, enhancement and synthesis. Course includes extensive Matlab exercises.

Date Topic Handouts Homework
3 Sep
10-12am		 Intro / the visual world / imaging Course description
Background poll 		 
11 Sep
4-6pm		 2D Convolution    
18 Sep
4-6pm		 2D Fourier Transform   HW1, due Oct 1
24 Sep
10-12am		 Sampling    
1 Oct
10-12am		 (Discrete) Differentiation    
8 Oct
10-12am		 Derivative design / rotation-invariance    
15 Oct
10-12am		 Orientation estimation Conf paper
Draft journal paper 		 
22 Oct
10-12am		 Matching/registration   HW2, due Oct 29
29 Oct
10-12am		 Statistical image models    
5 Nov
10-12am		 Estimation basics    
12 Nov
10-12am		 Spectral models    
19 Nov
10-12am		 Denoising with spectral models    
3 Dec
10-12am		 Denoising with local bases slides  
5 Dec
12:30-2pm		 multi-scale decompositions    
12 Dec
12-2am		 wavelets Book chapter  
17 Dec
10-12		 wavelets    

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.