Home » Digital Image Processing (ECE 6258)

Digital Image Processing (ECE 6258)

Course Primary Instructor:

Prof. Ghassan AlRegib

  • Office: Centergy Room 5224
  • E-mail: alregib@gatech.edu
  • Tel: +1 (404) 894-7005
  • WebEx Room: https://gatech.webex.com/meet/ga42 (734 000 569)
  • Course Days/Times: T, TH 4:30 – 5:45 PM
  • Class Location: KLAUS 2443
  • Office Hours:
    Thursdays, 01:00-02:00PM in Office (Centergy-I Room 5224). The fifth floor is access controlled. For students to gain access to the 5th floor during office hours, they must use the phone in the lobby on the 5th floor to contact one of the ECE Staff personnel whose phone numbers and names are posted on the directory in the lobby on the 5th floor. After students provide the staff member with a valid reason for gaining access, a staff member will open the door for the studentThursdays, 05:45-06:15PM, after lecture in Klaus hallway Or by Appointment for in-person meeting or via WebEx
    (https://gatech.webex.com/meet/ga42)

Course Staff:

Mr. Motaz Alfarraj

  • E-mail: motaz@gatech.edu
  • Office Hours: by appointment only
  • Responsibilities: Piazza, Projects, and Lectures Delivery

Mrs. Yuting Hu

  • E-mail: huyuting@gatech.edu
  • Responsibilities: Problem Sets, Piazza, Exams, and first to be contacted for any question about the class
  • Office Hours: W,TH 9:30 AM – 11:00 AM
  • Location: Klaus Event Spaces Conference Room 2100 (Wednesday), Clough Room 146 (Thursday)
    WebEx Room: https://gatech.webex.com/meet/yhu75 (731 806 170)

Special Dates:

  • October 10, 2017: no class; Students Fall Recess
  • October 28, 2017: last Day to withdraw with ‘W’
  • November 23, 2017: no class; Thanksgiving Holiday
  • December 05, 2017: Final Instruction Class Day

Check the Registrar’s website for all correct and important dates. Check the tentative lecture schedule and timeline posted on T-square under the Syllabus Tab. The schedule will be updated frequently.

Grading:

Homework*** 20%
Exam #1* 20%
Exam #2* 20%
Project** 40%

Exam #1: Tuesday, September 26, 2017 (tentative)
Exam #2: Thursday, November 02, 2017 (tentative)
Final Exam: No Final Exam

* No Make-up exams. If you have to be absent for an exam, you need to inform me in advance with an official justification and your next exam/assignment will be counted for both. If the absence is caused by emergency, an official paperwork is required.
** The Project Description will be shared and explained on August 31, 2017.
*** No Homework assignments will be accepted nor graded after the posted due date and time have passed.

Due Dates:

All sections will have the same due dates for all assignments. This applies to the video section. The video section will be given a flexible window (typically one week) to take the exams.

Attendene:

Attendance: Your attendance and participation are strongly encouraged. There has been a strong correlation between attending lectures and the earned letter grade in this class.

T-square:

All announcements will be posted on T-square. Check T-Square on a daily basis.

T-Piazza:

Students are expected to utilize PIAZZA platform to post questions and engage into online discussions. Students are encouraged to post questions through PIAZZA before attempting to email any of the instructors.

Assignments Submission:

All homework assignments need to be submitted on T-square. Certain assignments (e.g. project codes, posters … etc.) may require submissions through another platform, which will be clearly specified in the instructions of each assignment. Read the instructions of each assignment carefully.

Travel Dates:

Travel Dates: I will be attending a number of technical conferences throughout the semester. During these travel times, the Course staff will deliver the lectures.

Academic Honesty:

All violations of the Georgia Tech Honor Code will be handled by referring the case directly to the Dean of Students for investigation and penalties. The complete honor code can be found in the GT Policy Library: http://www.policylibrary.gatech.edu/student-affairs/academic-honor-code

Available Resources:

Office of Disability Services:

If you are a student registered with the Office of Disability Services (ODS), please make sure the appropriate forms and paperwork are completed with the instructor within the first week of classes. The instructor will abide by all accommodations required by ODS. The schedule for exams is posted in the syllabus and any potential modifications or changes will be made with at least one week’s notice. It is the responsibility of the student to properly arrange test accommodations for each exam with ODS in sufficient time to guarantee space for exam administration. ALL exam accommodations must be handled through ODS. If the student does not register accommodations with ODS for the taking of an exam, then they will have to take the exam at the normally scheduled times without any additional accommodation unless the instructor is given specific directive from ODS on the student’s behalf due to a mitigating circumstance.

Prerequisite:

A course in digital signal processing (ECE4270 or equivalent). I expect students to be familiar with MATLAB®.

Course Objective:

To introduce the fundamentals and the theory of multidimensional signal processing and digital image processing, including key applications in multimedia products and services including machine learning.

Textbook and References:

No required textbook but the following books are excellent references for this class:

  1. R.C. Gonzalez and R.E. Woods, Digital Image Processing, 3rd edition, Prentice-Hall, 2008 (officially the textbook of the course)
  2. M. Petrou and C. Petrou, Image Processing: The Fundamentals, 2nd Edition, Wiley, 2010 (helpful reference in the first half of the semester)
  3. J. W. Woods, Multidimensional Signal, Image, and Video Processing and Coding, 2nd Edition, Academic Press, 2012
  4. A.K. Jain, Fundamentals of Digital Image Processing, Prentice-Hall, 1989
  5. J.S. Lim, Two-dimensional Signal and Image Processing, Prentice-Hall, 1990
  6. M.J.T. Smith, A. Docef, A Study Guide for Digital Image Processing, Scientific Pub., 1999

Tentative Course Schedule and Timeline

Week Date Lecture Topic Course Materials
1 22-Aug Introduction / Syllabus [PDF] [Support1] [Support2]
24-Aug Multidimensional Signals and Operations [PDF] [Code] [Support]
2 29-Aug Multidimensional Signals and Operations [PDF] [Code] [Support]
31-Aug 2D Frequency [PDF] [Code] [Support]
3 5-Sep 2D Frequency; Sampling [PDF] [Code] [Support]
7-Sep Sampling [PDF] [Code] [Support]
4 12-Sep Image Transforms – DFT [PDF] [Code] [Support]
14-Sep  Image Transforms –DFT and DCT [PDF] [Code] [Support]
5 19-Sep Image Transforms –KLT [PDF] [Code] [Support]
21-Sep  Wavelets and Directional Transform
6 26-Sep Exam #1 (Tentative)
29-Oct Image Coding –JPEG
7 3-Oct Video Coding; Motion Estimation [PDF] [Code] [Support]
5-Oct Motion Estimation; Optical Flow [PDF] [Code] [Support]
8  10-Oct No Class (Fall Recess)
 12-Oct Image/Video Quality Assessment [PDF] [Code] [Support]
9 17-Oct Image Enhancement, Image Denoising [PDF] [Code] [Support]
 19-Oct Image Denoising, Image Restoration [PDF] [Code] [Support]
10  24-Oct Edge/Point/Line Detection [PDF] [Code] [Support]
 26-Oct Image Segmentation [PDF] [Code] [Support]
11  31-Oct Saliency and Attention Models [PDF] [Code] [Support]
 2-Nov Exam #2 (Tentative) [PDF] [Code] [Support]
12 7-Nov Introduction to Machine Learning (ML) [PDF] [Code] [Support]
9-Nov Basic Functions for Images Using ML
13  14-Nov Basic Functions for Images Using ML; Fundamentals of Linear Classifiers [PDF] [Code] [Support]
16-Nov Fundamentals of Linear Classifiers & SVM [PDF] [Code] [Support]
14  21-Nov Convolutional Neural Networks [PDF] [Code] [Support]
 23-Nov No Class (Thanksgiving Holiday) [PDF] [Code] [Support]
15  28-Nov Overview of LSTM, RNN, and NTM for Image Related Applications [PDF] [Code] [Support]
30-Nov Scene Labeling [PDF] [Code] [Support]
16  5-Dec Poster Session for Term Projects [PDF] [Code] [Support]
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