• Course Description Various techniques to enhance, deblur, segment, and describe image features will be introduced. This course will also present fundamentals of digital image formation, color models, halftoning, and compression, and will include project based implementation of these techniques. Students will be encouraged to develop application-specific modules for medical, satellite, and natural images. Topics will include edge detection, morphological processing, texture analysis, feature extraction, sampling and transforms, and image watermarking.
  • Prerequisites: Programming experience in C/C++ or Matlab, basic understanding of signal processing and probability.
  • Instructor: David R. Carey Adjunct Professor Electrical Engineering
  • Office: SLC-214 Phone:570-408-4807 Email: david.carey@wilkes.edu
  • Lecture: 6:00 pm - 8:45 pm - SLC 216 - Mondays
  • Instructor Office Hours: By appointment only, email me or call me for an apppointment.
  • Textbook: Digital Image Processing Using Matlab by Gonzalez, Woods, and Eddins, Prentice Hall (required). (ISBN 0-130-08519-7)
  • Reference Books:
    1. A. Bovik, Ed., Handbook of Image and Video Processing, Academic Press, NY 2000.
    2. J.C. Russ, The Image Processing Handbook, 3rd Edition, IEEE Press, 1999.
    3. W.K. Pratt, Digital Image Processing- PIKS Inside, 3rd Edition, John Wiley, 2001.
    4. S.E. Umbaugh, Computer Vision and Image Processing ˙ã a practical approach using CVIPtools, Prentice Hall, 1998.
  • Course Website: http://course.wilkes.edu/EE498DIM
  • Course Objectives:
    1. Students will learn various digital image processing techniques and their applications.
    2. Students will get hands-on experience in implementing many of these techniques for manipulating digital images.
  • Homework: There will be 4 problem sets. No late homework will be accepted. Homework assignment should be solved using pen (do not use erasable pencil for Homework and Examinations). Make-up Exam shall be given only in exceptional situations, with prior permission.
  • Grading Policy (for EE 498):
    1. Homework: 10% (due at 6:30 pm in the class room on the announced dates)
    2. Test 1: 20% (Feb. 16,, 7:00-8:00 p.m.), SLC-216
    3. Test 2: 20% (March 30, 7:00-8:00 p.m.), SLC-216
    4. Final Exam: 25%
    5. Term paper: 25% for Graduate students.
    6. Classroom Performance: 5% (bonus for participating in the discussions during lecture)
  • Topics:
    1. Digital image fundamentals
    2. Image Enhancement and Noise Removal
    3. Color image processing
    4. Morphological image processing
    5. Image Segmentation
    6. Representation and Description
    7. Object Recognition
    8. Image Compression
    9. Wavelets and Multiresolution processing
    10. Image Watermarking
  • Course Learning Outcomes: Students will have the knowledge and show competence in the use of various digital image processing techniques.

Lecture Notes

Permanent link to archive for 1/31/09. Saturday, January 31, 2009
Based on The Lectures of Matthew Zukoski

Written/Transcribed by Kristopher Smith


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