Course Info Fall 2021
Course Objective: An introduction to the fundamentals and the theory of multidimensional signal processing and digital image processing, key applications in multimedia products and services including machine learning.
Course Primary Instructor: Prof. Ghassan AlRegib (alregib@gatech.edu)
Course Days/Times: T&TH 12:30-01:45PM
Class Location: KLAUS 2443
We will meet in person in class for lectures. In some days, there will be videos that I would like you to watch before class. Depending on the lengths of such videos, watching them may constitute a lecture. In such a case, we will not meet during lecture hour. You will be informed at the beginning of the week to plan your lectures attendance and/or video watching. Therefore, it is crucial to check the schedule on Canvas, on a daily basis. Students are expected to be familiar with and abide by the Institute guidelines, information, and updates related to Covid-19. Find campus operational updates, Frequently Asked Questions, and details on campus surveillance testing and vaccine appointments on the Tech Moving Forward site.
Office Hours: Mondays and Thursdays, 11:00am-12:00pm
check location(s) on canvas.
Course TAs:
Ahmad Mustafa (amustafa9@gatech.edu) Office Hours: TBD
Xinhui Li (xli993@gatech.edu) Office Hours: TBD
Communications: Avoid emails. You can use PIAZZA to send a message to me or to me & the TA(s).
Announcements: Official announcements will be posted on Canvas, via Piazza, or announced during lectures.
Textbook: No required textbook
References:
1. R.C. Gonzalez and R.E. Woods, Digital Image Processing, 3rd edition, Prentice-Hall, 2008
2. M. Petrou and C. Petrou, Image Processing: The Fundamentals, 2nd Edition, Wiley, 2010
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
Prerequisite: For graduate students: a course in digital signal processing (e.g., ECE4270 or equivalent). For undergraduate students: ECE 2026 [min C] (or equivalent) and Prerequisites with concurrency: ECE 3077 [min C] or ISYE/MATH/CEE 3770 [min C] or MATH 3670 [min C]
I expect students to be familiar with programming.
Canvas: Go to https://canvas.gatech.edu/ and if you do not see the class page, make sure you are registered for the course.
Piazza: Students are expected to utilize PIAZZA platform to post questions and engage into online discussions. Make sure you enroll into the course site on Piazza via this URL: https://piazza.com/gatech/fall2021/ece48036258. If you have any difficulty, you can email team@piazza.com.
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
Campus Support: Dean of Students Office, CARE Center, Counseling Center, Stamps Health Services, and the Student Center: The CARE Center and the Counseling Center, Stamps Health Services, and the Dean of Students Office will offer both in-person and virtual appointments. Student Center services and operations are available on the Student Center website. For more information on these and other student services, contact the Dean of Students or the Division of Student Life.
Student Illness or Exposure to Covid-19: Refer to the bottom of this web page: https://provost.gatech.edu/academic-restart-frequently-asked-questions
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.
Grading:
For Undergraduate students:
Homework (drop lowest) 50%
Exams 1+2 20+30=50%
Project* 10%
For Graduate students:
Homework (drop lowest) 45%
Exams1+2 15+15=30%
Project 25%
* The project for undergraduate students will be optional and graded for a bonus credit of 10%.
Exams Dates (subject to change):
Exam #1: September 23, 2021
Exam #2: November 04, 2021
Final Exam: No Final Exam
Remarks:
- No Homework assignments will be accepted nor graded after the posted due date/time
- No Make-up exams. If you have to be absent for an exam, you need to inform me in advance with an official justification, i.e., official paperwork.
- All exams will be digital. All students in all sections will take the exam within a specific Window. For all sections, except Section Q, the window is within the day of the exam. Section Q students will be given larger window that starts on the exam day and ends during the weekend.
Due Dates: Assignments are due on Fridays @5pm, unless stated otherwise, for all sections except Section Q. Section Q will have a 3-day delay, until the following Monday @5pm.
Assignments Submission: All homework assignments need to be submitted online. Read the instructions of each assignment carefully. Most assignments will be a mix of theoretical questions and programming exercises—the former to test the students’ grasp of lecture concepts and the latter to evaluate their proficiency in applying those concepts to use cases.
Programming Language: The homework assignments have hands-on exercises that involve programming. I encourage the utilization of Python. We have prepared a library called dippykit to help you perform such assignments, https://dippykit.github.io/dippykit/. Prior familiarity with Python is preferred but not necessary. Students who are familiar with Matlab® but would like to use this opportunity to learn Python may find this tutorial helpful. Instructions to set up your PC to run python scripts can be found on the dipykit page. Alternative setup, using Anaconda, can be found on this page. In any case, students are free to use other languages/packages such as C/C++ and Matlab®.
Term Project:
Teams: Students must work on the term project in teams of FOUR students; no exception. If we end up with an odd number of students, we may allow one team to have five students. You may utilize Piazza to team up.
Topics: The project must be related to image processing and must focus on one or more of the following:
- Image/Video Interpolation
- Image/Video Quality Assessment
- Image/Video Compression
- Image/Video Enhancement and Denoising
The specific topic is to be chosen by the students.
Proposal (15%): On October 01, 2021, every team must submit a one-page proposal that contains the title, the names of the team members and their GT IDs with their email addresses, a summary of the proposed project, a tentative, but close to the actual, timeline, a brief discussion of the expected obstacles, a plan of the tasks for every team member, and the planned delivered results and outcomes. You can list all references in a second page, including links to codes that will be used in the project. The proposal must include the choice of final poster presentation or final poster AND term paper (Read Below). This choice is final and cannot be modified after October 1st.
Progress Report (25%): On November 13, 2021, teams are expected to submit a one-page progress report that details the completed tasks, details of the individual’s work, and any planned modifications on the original plan in the proposal with justification. During class, on November 12, 2021, the teams will give 7-minute presentations on the progress.
Poster Presentation (30% or 60%): Teams are expected to present their final term project on December 7th during a 2.5-hour virtual poster session, between 12-2:30pm. Teams who choose to be evaluated based only on the poster are expected to provide a thorough presentation followed by Q&A, ~20 minutes in total. Teams who choose to be evaluated based on the poster AND the term paper will have a 10-minute poster presentation including Q&A.
Term Paper (30% or 0%): Every team can choose and decide whether to be evaluated (60% of the project grade) on the poster presentation alone or the poster presentation AND the term paper (in which case each item would be 30% of the project grade). The term paper is due on December 7, 2021 @11am. The term paper must follow an IEEE Double-column, single-space, 11-pt font size format. Use a Letter size template. A template can be found HERE.
Project Grade Distribution:
Proposal: 15%
Progress Report: 25%
Poster Presentation: 30% (or 60%)
Term Paper: 30% (or 0%)
*Undergraduate students who choose to do the project for a bonus of up to 10% will be evaluated based on a poster AND a term paper. Undergraduate students can work on in groups of sizes 1 to 4 members. Such intentions and teams must be declared as a proposal to be submitted no later than November 12, 2021.
