ECE 847 Digital Image Processing
Fall 2004

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 I HW1:  Warm-up, due 8/27
2 Pixel-based processing II HW2:  Processing pixels, due 9/3
3 Filters and edge detection I  
4 Filters and edge detection II HW3:  Point distribution models, due 9/17
5 Stereo  
6 Motion HW4:  Head tracking, due 10/1
7 Transforms I  
8 Transforms II HW5:  Stereo matching, due 10/15
9 Pattern detection  
10 Segmentation HW6:  Lucas-Kanade tracking, due 10/29
11 Texture start projects
12 Projective geometry
13 Image formation
14 Advanced topics
15 Project presentations 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:

Final project reports from our Fall 2004 internal mini-conference:

  1. Particle filter tracking with Isomap
  2. Image registration
  3. Background subtraction using color and gradient information
  4. Shape descriptor using polar plot for shape recognition
  5. 3D photography using shadows
  6. Digital image restoration techniques and automation
  7. Elliptical head tracker using intensity gradients and texture histograms
  8. Image Compression: Review and Comparison of Haar Wavelet Transform and Vector Quantization
  9. Facial Feature Detection and Gaze Tracking
  10. Fingerprint image enhancement and minutiae extraction
  11. Real time head tracker using frame grabber and a webcam
  12. Reading Barcodes Etched on Shiny Surfaces Using Basic Image Processing
  13. Face recognition using eigenfaces
  14. Skeet tracking:  counting hits and misses
  15. Simultaneous localisation and mapping with a single camera
  16. Semi-automated feature based image morphing using graph cuts
  17. Image Morphing: Feature based, View and Mesh
  18. Head tracking using stereo
  19. Computer vision for computer games
  20. Tracking of objects in spatiotemporal volume by graph-cuts
  21. Hand recognition and tracking
  22. Star sensor:  Star pattern recognition
  23. (omitted)
  24. Image synthesis for a CT simulator
  25. Vehicle boundary detection and tracking
  26. Tracking using intensity gradients and particle filtering
  27. Iris recognition for personal identification
  28. Image morphing:  a comparative study
  29. Head tracking using learned linear subspaces





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: 1:25 - 2:15 MWF, 307 Riggs Hall