CSE 576: Image Understanding

Autumn 1998

Instructor: Linda Shapiro
TA: Doug Zongker

Syllabus

Purpose of Course:

To introduce the topic of Computer Vision to graduate students. The course will discuss all three levels of computer vision: early processing, mid--level vision or feature extraction, and high--level vision or recognition. We will cover some basic material, but will emphasize state-of-the-art techniques and advanced applications.

Text:

New Computer Vision Textbook, by Shapiro and Stockman. Hot off the presses, in fact, it hasn't even gotten to the real presses. All finished chapters will be handed out, and are available online. Comments are welcome.

Also, both Vol. 1 and Vol. 2 of the 1992 Haralick and Shapiro text Computer and Robot Vision should be on reserve in the library as is the Jain, Kasturi, and Schunk text, Machine Vision. This latter text is good reading for students who have little or no previous background in computer vision / image processing.

Programming Language:

Doug is putting together a set of C programs for some important low-/mid-level operations that we often use in 3D object recognition. These will available for downloading from the course web and use on PCs. Students should be able to write their own C/C++ programs to add additional capabilities as needed.

Project:

Each student will propose, design, and implement a program that does some kind of machine vision. Possible topics and more specific requirements are available here. The project is geared to take 4-5 weeks and requires a 5-10 page report describing the program and the results.

Topics to Cover:

We will talk about what to skip, what basics to cover, and what advanced applications are of most interest. The chapters of the text and their present states are given below as a guide:
  1. Introduction
  2. Imaging and Image Representation
  3. Binary Image Analysis
  4. Pattern Recognition Concepts
  5. Filtering and Enhancing Images
  6. Color
  7. Texture
  8. Content-Based Image Retrieval
  9. Motion*
  10. Image Segmentation*
  11. Matching in 2D
  12. Perceiving 3D from 2D Images
  13. 3D Sensing+
  14. 3D Models and Matching
  15. Virtual Reality
  16. Related Topics*
* = not yet written
+ = not yet finished, but coming along well

Evaluation:

The grades will be based on a combination of small homework sets, midterm, final, and project. Projects will be due on the last day of class, so I have ample time to grade them.