EE 264 - Image Processing and Reconstruction

Winter 2005

Instructor:

Peyman Milanfar

Office:

Engineering II , Room 243A

Phone:

(831) 459-4929

email:

milanfar AT ee DOT ucsc DOT edu

Lecture:

T/Th 10:00 to 11:45, BE 372

Office Hours:

T/Th 1 to 2

Required Text:

Digital Image Processing by Gonzalez and Woods, Second Edition (Errata)

Reference Texts:
  • Digital Image Processing Using MATLAB by Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins
  • Digital Image Processing by William K. Pratt
  • Fundamentals of Digital Image Processing by Anil K. Jain
  • Two-Dimensional Imaging by Ronald N. Bracewell

Grading Policy:

Homeworks (20%), Midterm (30%), Final Project (50%)

Notes:

Homework exercises will require the use of the software package MATLAB. Here is a primer.

 

Important Dates:

First day of class

Tuesday, January 4

Last day of class

Thursday, March 10

Holidays

Monday, January 17, Monday , February 21

Midterm examination

Tuesday, February 22

Final Project reports due

Thursday, March 10

Final Project presentations

Tuesday, March 15, 1:00-4:00 PM (E2, Room 215)

Lecture Notes:

  1. Overview
  2. Review
  3. Image Formation, Sampling, and Resolution
  4. Point-wise Operations
    1. Retinex
  5. Local Operations in the Image Domain
  6. 2-D Signals and Systems, Matrix Formulations
  7. The Frequency Domain
  8. Filtering in the Freq. Domain, Sampling.
  9. Some additional notes on the frequency domain
  10. Restoration I: Description and basics of freq. domain approach
  11. Restoration II: Power Spectra and the Wiener Filter
  12. Restoration III: Basics of Pixel-domain Restoration and Statistical Methods
  13. Restoration IV: Advanced Pixel Domain Restoration, Implementation
  14. Motion Estimation I: Intro, models and block-based matching
  15. Motion Estimation II: Optical Flow motion estimation
  16. Multiresolution Image Processing
  17. Introduction to Compression
  18. Indirect Imaging: Tomography
  19. Introduction to Image Analysis
  20. Color Imaging

 Homeworks:

 

Term Project:

Course Announcements and Handouts:

Tentative Syllabus and Reading: 

 

 

Academic Dishonesty and Cheating:

Any confirmed academic dishonesty including but not limited to copying homeworks or cheating on exams, will result in a no-pass or failing grade. You are encouraged to read the campus policies regarding academic integrity. Examples of cheating include (but are not limited to):

  • Sharing or copying results or other information during an examination.
  • Working on an exam before or after the official time allowed.
  • Submitting homework that is not your own work.
  • Reading another student's homework solution before it is due.
  • Allowing someone else to read your homework solution before the assignment is due.

If there is any question as to whether a given action might be construed as cheating, see me before you engage in any such action.