ECE 877 Computer Vision
Spring 2007

This course builds upon ECE 847 by exposing students to fundamental concepts, issues, and algorithms in digital image processing and computer vision. Topics include segmentation, texture, detection, 3D reconstruction, calibration, shape, and energy minimization. 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 in more detail in a course project.



Week Topic Assignment
1 Shape and active contours HW1:  Template matching, due 1/19
2 Shape and active contours Quiz #1
3 Level sets HW2:  Active contours, due 2/2
4 Classification Quiz #2
5 Classification HW3:  Level sets, due 2/16
6 Fourier transform Quiz #3
7 Texture HW4:  Object detection, due 3/2
8 3D reconstruction Quiz #4
9 Camera calibration HW5:  Texture, due 3/16
10 [break] Quiz #5
11 Model fitting  
12 Tracking and filtering HW6:  Head tracking, due 4/6
13 Scale space and SIFT features Quiz #6
14 Function optimization  
15 Function optimization projects due

Readings and Resources

See ECE 847 Readings and Resources.

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 are encouraged to use the latest version of the Blepo computer vision library.

To make grading easier, your code should do one of the following:

To turn in your assignment, send a blank email to with the subject line "ECE877-1,#n" (without quotes), where 'n' is the assignment number; and cc the instructor and grader.  (No one reads the body of the email, so anything there will be ignored.)  You must send this email from your Clemson account, because the assign server is not smart enough to know who you are if you use another account.  Attach a zip file containing your report, all of your source files, and any other files needed to compile your project, to the email:  *.h, *.c, *.cpp, *.rc, *.dsp, *.dsw.  (Do not include the res, Debug, or Release directories.)  Also be sure your report, which may be in any standard format (.pdf, .doc, etc.), is in this directory.  Be sure that this file is actually attached to the email rather than being automatically included in the body of the email (This behavior has been observed in Eudora, for example, but it can be turned off).  Also, be sure to change the extension of your zip file (e.g., change .zip to _zip) so that the server does not block the attachment!!!  We cannot grade what we do not receive.  

An example report


Grading standard: 

Detailed grading breakdown is available in the grading chart

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: Zhichao Chen, 015 Riggs Hall, 650-0308, email: zhichac at clemson
1:25 - 2:15 MWF, 301 Riggs Hall