Course Description and Prerequisites
EE 635 is a graduate-level course outlining applications of image processing and addressing basic operations including image perception, transformations, compression, enhancement, restoration, segmentation, and matching. The course draws on Dr. Lau's research experience in printer design and halftoning to relate basic concepts to real consumer imaging problems encountered in industry. Prerequisites: Graduate standing and consent of instructor. Learning Outcomes
Upon completion, students should demonstrate the ability to:
Suggested Readings:
EE 635 is a graduate-level course outlining applications of image processing and addressing basic operations including image perception, transformations, compression, enhancement, restoration, segmentation, and matching. The course draws on Dr. Lau's research experience in printer design and halftoning to relate basic concepts to real consumer imaging problems encountered in industry. Prerequisites: Graduate standing and consent of instructor. Learning Outcomes
Upon completion, students should demonstrate the ability to:
- Demonstrate a basic understanding of image capture devices.
- Perform basic filtering operations for image enhancement, edge detection, and color segmentation.
- Demonstrate a basic understanding of image compression based on linear transforms such as the DCT and DWT.
- Perform simple feature detection using correlation-based filtering.
Suggested Readings:
- Fundamentals of Digital Imaging, H. J. Trussel and M. J. Vrhel, Cambridge University Press.
- Digital Image Processing, 3rd Ed., R. C. Gonzalez and R. E. Woods, Prentice Hall.
- Digital Image Processing, W. K. Pratt, Wiley-Interscience.
- Image formation, human visual perception, and color (including TIFF format, color spaces, CMYK)
- 2-D Fourier space, sampling, reconstruction, and interpolation
- Image enhancement and filtering
- Image compression, quantization, and transforms (DCT, DWT)
- Image segmentation and detection (correlation-based filtering)
- Three in-class exams (20% each)
- Comprehensive final exam (40%)
| Semester | Materials |
|---|---|
| Spring 2003 | Two syllabi, Exam I and Exam II (PDF + LaTeX source + figures), Exam I solutions, student color-space conversion programming projects (C source) |
| Spring 2004 | Syllabus, Exam I (PDF + LaTeX source) |
| Spring 2005 | Syllabus, Exam II, Exam III, and comprehensive final exam (all with PDF + LaTeX source), supporting figures |
| Spring 2009 | Syllabus, class topics list, student project folders (6 students), color perception lecture slides |
| Spring 2010 | Syllabus, three semester exams + final exam (all with PDF), figures |
| Spring 2011 | Syllabus, three semester exams + final exam, sample images (TIFF, JPEG), figures |
| Spring 2012 | Syllabus, three semester exams + final exam, figures, sample images |
| Spring 2013 | Syllabus, color lecture slides (2 sets), Keynote lecture files, Qt programming projects |
| Spring 2014 | Syllabus, color lecture slides, Keynote lectures, Qt projects, student folders |
| Spring 2015 | Syllabus |
| Fall 2017 | Syllabus |
| Fall 2024 | Syllabus |
| Shared Resources | Hand-written lecture notes compilation, teaching statement (IEEE two-column format with LaTeX source) |
