University of Wisconsin - Madison
Department of Electrical and Computer Engineering

ECE/CS 738 Advanced Digital Image Processing

Course Description

Spring 2007 Semester

Course Information

• Instructors: Yu Hen Hu , 3625 Engr. Hall, Tel.262-6724, E-mail: hu@engr.wisc.edu
• Place and time: 9:55-10:45 AM, MWF, 3444 Engineering Hall
• Prerequisite: ECE 533 or consent of instructor.
  • It is strongly suggested that students successfully complete ECE533 before taking this course. Special permission can be made to advanced graduate students who can demonstrate in-depth knowledge in basic image processing topics.
  • Students are expected to be familiar with the following concepts: Wiener filtering, constrained least square restoration, linear predictive coding, linear transform (DFT, DCT, DWT), context-based entropy coding, clustering and optimal vector quantization, 1D and 2D linear system thoery, digtal filters, 2D convolution, human visual system and visual psychophysics, image segmentation, edge detection.
• Goals: To learn advanced topics in image and video processing through paper reading, group discussion, report writing and special project.
• Topics:
  • Visual pattern recognition, shape recognition, with emphasis on human face recognition
  • Data hiding, secure multimedia communication, water marking
  • Visual information communication, video streaming, scalable video coding, error concealment, uneven error protection
  • image/video analysis and content based retrieval: image and video segmentation, content-based information retrieval, natural and synthetic image coding, image and video fetaure extraction
• Textbook: A set of papers will be posted on password-protected web page.  Reference books will be assigned and placed in Wendt library on reserve.
• Computer Usage: Ability to program in Matlab and C is expected. Experiences in manipulating, presenting, and playing image and video files are desirable.
• Grading Policy:
  • 10 % Paper presentation, paper summary, contribution to in-class discussion and participation.
  • 30 % Assigned short projects. Tentatively three.
  • 20 % Final Examination
  • 40 % Individual project

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