ECE 893-3 Machine Vision
Spring 2006

This course builds upon ECE847 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:  due 1/20
2 Shape and active contours  
3 Classification HW2:  due 2/3
4 Classification  
5 Fourier transform HW3:  due 2/17
6 Texture  
7 3D reconstruction HW4:  due 3/3
8 Level sets  
9 Camera calibration HW5:  due 3/17
10 [break]  
11 Model fitting  
12 Tracking and filtering HW6:  due 4/7
13 Scale space and SIFT features  
14 Function optimization HW7:  due 4/21
15 Function optimization  


In your project, you will investigate some area of image processing or computer vision in more detail. This will involve formulating a problem, reading the literature, proposing a solution, implementing the solution, evaluating the results, and communicating your findings.

Possible project topics:



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:


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: 1:25 - 2:15, 301 Riggs Hall