ECE 847 Digital Image Processing
Fall 2005

This course introduces students to the basic concepts, issues, and algorithms in digital image processing and computer vision. Topics include image formation, projective geometry, convolution, Fourier analysis and other transforms, pixel-based processing, segmentation, texture, detection, stereo, and motion. The goal is to equip students with the skills and tools needed to manipulate images, along with an appreciation for the difficulty of the problems. Students will implement several standard algorithms, evaluate the strengths and weakness of various approaches, and explore a topic of their own choosing in a course project.



Week Topic Assignment
1 Pixel-based processing HW1:  Warm-up, due 9/2
2 Pixel-based processing Quiz #1, 9/9
3 Filters and edge detection HW2:  Pixels and regions, due 9/16
4 Filters and edge detection Quiz #2, 9/23
5 Segmentation HW3: Canny edge detection, due 9/30
6 Segmentation Quiz #3, 10/7
7 Stereo HW4: Split-and-merge segmentation, due 10/14
8 Stereo Quiz #4, 10/21
9 Motion HW5: Stereo matching, due 10/28
10 Motion Quiz #5, 11/4
11 Image formation HW6:  Lucas-Kanade tracking, due 11/11
12 Projective geometry Quiz #6, 11/18
13 Projective geometry
14 Color Quiz #7, 12/9
15 Color projects due


Readings to complement the lectures:

Computer vision in the news:

Vision in biological systems:


In the assignments, you will implement several fundamental algorithms in C/C++, documenting your findings is an accompanying report for each assignment.  The C/C++ languages are chosen for their fundamental importance, their ubiquity, and their efficiency (which is crucial to image processing and computer vision).   For your convenience, you may use the latest version of the Blepo computer vision library.



Extra credit:  Contributions to the Blepo computer vision library will earn up to 10 points extra credit on your final grade.  In general, you should expect 1 point for a major bug fix, and 2-7 points for a significant extension to an existing function or implementation of an algorithm or set of functions.  Contributions should be cleaning written, with code-level and user-level documentation, and a test harness.  To receive extra credit, you must meet the following deadlines:



In your final project, you will investigate some area of image processing or computer vision in more detail. Typically this will involve formulating a problem, reading the literature, proposing a solution, implementing the solution, evaluating the results, and communicating your findings. In the case of a survey project, the quality and depth of the literature review should be increased significantly to compensate for the lack of implementation.

Project deadlines:


Instructor: Stan Birchfield, 207-A Riggs Hall, 656-5912, email: stb at clemson
Grader: Prashant Oswal, email:  prashao at clemson (please use this account, not his regular account)
Lectures: 12:20 - 1:10 MWF, 301 Riggs Hall