Math 733: Introduction to Image Analysis
Time: Mon. & Wed., 10-11:30
Location: Dunham Lab. (DL) 431.

The first meeting is Monday, 01/22/07.

Instructor: Triet M. Le
Email: firstname.lastname@yale.edu.
Phone: (203)432-4011
Office: DL 418
Office Hours: By appointment.
Course Description:

An important problem in Image Analysis is the seperation of texture or noise from piecewise smooth objects having sharp boundaries. We can think of this problem as the decomposition of an image f into a piecewise smooth part u, containing the geometric components of f, and an oscillatory part v, containing texture or noise. This has direct applications to the reconstruction of noisy or blurry images. There are two well known approaches to this problem: 1) Morphological methods, and 2) Variational methods. This class concerns the variational approaches to this problem and the connection to Bayesian statistical methods.

We can think of f as an element in some normed space X. The decomposition of f into u + v, where u and v belong to the spaces X1 and X2, can be considered as an interpolation of the space X into X1 + X2. Given the properties of u and v, what are the appropriate choices for the normed spaces X1 and X2 and the operators acting on them? This class addresses this question and the computational aspects involved. Moreover, we will examine the structure of the various function spaces as well as various methods of representing them.

There is no prerequisite for this class, although a basic knowledge of real analysis and measure theory is useful.

Book References:
Some Paper References: