ECE 893-2 Machine Vision
Spring 2005

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 of their own choosing in a course project.




Week Topic Assignment
1 Texture HW1:  Warm-up, due 1/21
2 Texture HW2:  Texture, due 1/28
3 Segmentation  
4 Segmentation HW3:  Segmentation, due 2/11
5 Detection  
6 Detection HW4:  Detection, due 2/25
7 3D reconstruction  
8 3D reconstruction HW5:  3D reconstruction, due 3/18
9 Camera calibration  
10 [spring break]  
11 Range images HW6:  Calibration, due 4/1
12 Object recognition start projects
13 Shape and active contours
14 Energy minimization
15 Energy minimization projects due


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: Sunil Guduru, email:  guduru at clemson (Note:  Please use this email address, not Sunil's regular one)
Lectures: 9:05 - 9:55 MWF, 305 Riggs Hall