CS 422/522: Digital Image Processing

Instructor: Lance Williams <williams@cs.unm.edu>
Time: Tues 12:30-1:45, Thurs. 12:30-1:45
Location: Mitchell 206
Office Hours: Mon. 3:00-5:00, Tues. 3:00-5:00
Office: FEC 349C

This course will provide an introduction to the fundamentals of digital image processing. Specific topics include grey level histograms, geometric and grey level transformations, linear systems theory, Fourier transforms, filter design, wavelet transforms, image compression, edge detection, color vision, and binary image morphology.

Understanding of image processing theory will be reinforced by hands-on exercises and programming assignments using UNM Scheme, an implementation of the Scheme programming language with real and complex image datatypes and an extensive library of functions for digital image processing. Alternatively, students may use Haskell to complete their assignments using the new UNM HIP image processing library.

If you want to run UNM Scheme from inside Emacs, you will want to include these definitions in a file called .emacs in your home directory.


The text for the course will be Digital Image Processing by Ken Castleman.





UNM Scheme


If you are enrolled in this course, it is assumed that you have taken CS 357 or have a level of familiarity with Scheme that is comparable to those who have. You should especially familiarize yourself with tail-recursion. If you need to brush up on your Scheme, I recommend the following online books:

When most people imagine coding in a digital image processing course they think of looping over rows and columns and touching pixels. This course is going to be different. Most of the coding drudgery has been done for you. This is a course on functional image processing. Because you will rarely (if ever) have to touch pixels yourself, you will be free to solve more interesting problems.


* Subject to change.

** The homeworks are due at the assigned times. The professor may, but is not obligated to, accept late submissions at a penalty of no less than 10% per 24 hours late.