It is worth mentioning at the outset some of my ideas regarding the nature of the course. Since this is not a Electrical Engineering ``core'' course, I feel that there is some leeway as to style and subject matter. The course ESE558. Digital Image Processing I, covers standard material that is deemed important in the sense that an EE student is responsible for this on Qualifier Exams. This course is independent of ESE558, and does not require ESE558 as a prerequisite.
In particular, what I'd like to do is to focus on very recent trends in the image processing and analysis area, with the ostensible goal that a motivated student would, after this course, be ready to participate in the field at a research level. The trend I choose to concentrate on is that part of image processing that may be addressed by statistical (Bayesian in particular) methods. This is not all of image processing, but it is surprisingly general, and the approaches may be generalized readily to new areas as they arise. I will try to indicate those reas of image processing that we are not covering.
Much of the actual material involves a review of both well-known and somewhat new applied mathematical methods. My idea is to keep the course self-contained, and to cover the methods within the course. Some homework problems will be review exercises, for example.
I encourage questions and discussion, and also point out ahead of time that I am easily capable of making errors since I am not an expert in all areas of the course material. I intend to go out on a limb with this course and try to cover very recent developments, so such snags may be inevitable.
Since there is no single text that covers this course, the notes will primarily be class notes. Copies will be available most likely. However, we shall make use of a few selected self-contained chapters from various books. (Copies available.) In all cases, I will make every effort to maintain consistent notation and presentation, and to ensure that the subject matter is backed up by substantial notes or published material.
The class project will be discussed in more detail, and I'll have more to say after the first class. However, based on my teaching a version of this course 2 years ago, I observed that the projects were quite tractable. The idea is that the student should focus on one topic, do some actual computational work with it, and write up a summary.