Curriculum / DescriptionsELEC_ENG 420: Digital Image Processing
This course is not currently offered.
Prerequisites
ELEC_ENG 359 or equivalent.Description
Course Goals
To study the application of digital signal processing to problems in image processing. Topics covered will range from the fundamentals of 2-D signals and systems, to image enhancement, restoration and segmentation.
Course Description
The field of Image Processing is concerned with the study of computational approaches for the analysis, storage and interpretation of digital content. In modern times, the sphere of influence of Image Processing has expanded to include an assortment of fields ranging from medical diagnostics to autonomous navigation. This course provides introduces students to basic concepts and techniques in digital image processing. Topics covered will include characterization and representation of digital images, image enhancement, image restoration, image analysis, and image segmentation.
Prerequisites
Desired: Introduction to Signal Processing or equivalent, Introduction to Linear Algebra or equivalent.
Required: Ability to program. The homework assignments and the exams will involve substantial amount of programming in MATLAB.
Textbook (Optional)
R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th Edition, Pearson, 2018
Time & Place
Tuesdays and Thursdays 1:00pm-2:20pm CT
ECE420 lecture: All lectures will held live on zoom and linked through canvas. Lectures will also be recorded for those who cannot attend during scheduled class times.
Instructors & Office Hours
Oliver Cossairt Office Hours: Thursday 3-5PM - write an email to oliver.cossairt (a) northwestern.edu to book a 10min slot.
Florian Willomitzer Office Hours: Thursday 3-5PM - write an email to florian.willomitzer@northwestern.edu to book a 10min slot.
Teaching assistants & Office Hours
Jiazhang Wang Mail: JiazhangWang2024 (a) u.northwestern.edu
Office hours: TBD.
Policies
-
Grading: Homeworks are worth 80% of the grade, and there will be 5 homework assignments worth 12.5% of your grade each. Assignments will consist of a coding and a technical writeup. Your coding must be correct, and your writeup must be clearly written. The Final is worth 20% of the grade and will be open-book and open-notes, but you may not discuss the test with anyone or consult with others. 100%-95% is an A, 95%-90% is an A-, 89%-85% is B, 84%-80% is B-, 79%-70% is C, 69%-60% is D.
-
When and Where to Submit Assignments: Your matlab code and a PDF writeup report for each assignment must be submitted on Canvas by 11:59pm on the due date.
-
Late Policy: All Homeworks are to be submitted via Canvas by 11:59pm on the due date. Each student will be permitted ONE late submission for partial credit. Two points shall be docked from the submission for each 24-hour period. For instance, if the homework is due Tuesday at 11:59pm and it is submitted Wednesday between 12:00am and 11:59pm, 2 points will be docked. If the assignment is submitted on Thursday between 12:00am and 11:59pm, 4 points will be docked, and so on. Only ONE late assignment per student will be awarded partial credit. Any additional late assignments will receive no credit.
-
Cheating & Academic Dishonesty: Do your own work. This includes free response answers and code. Penalties include failing the class and can be more severe than that. If you have a question about whether something may be considered cheating, ask, prior to submitting your work. We will be checking for code duplication. Academic dishonesty will be dealt with as laid out in the student handbook.
-
Attendance is mandatory but not graded.
-
Announcements and discussions will take place on Canvas.
Lecture Calendar
This is a prediction of what will be covered in each week but the schedule is subject to change as the course progresses.
| Week of | Lecture of week | Topic |
|---|---|---|
| 1/12 | Tue | Introduction |
| 1/14 | Thu | Image Formation |
| 1/19 | Tue | Mathematical tools for digital image processing |
| 1/21 | Thu | Histogram Processing |
| 1/26 | Tue | Spatial Domain Filtering I |
| 1/28 | Thu | Spatial Domain Filtering II |
| 2/02 | Tue | Fourier Domain Processing I: 1D Signals |
| 2/04 | Thu | Fourier Domain Processing II: 2D Signals (Images) |
| 2/09 | Tue | Image Restoration |
| 2/11 | Thu | Morphological Procesing |
| 2/16 | Tue | Edge Processing I: Detection |
| 2/18 | Thu | Edge Processing II: Hough Transform |
| 2/23 | Tue | Feature Extraction I: Corner Detection |
| 2/25 | Thu | Feature Extraction II: SIFT |
| 3/02 | Tue | Image Segmentation I: KMeans and Mean Shift |
| 3/04 | Thu | Image Segmentation II: Graph Methods |
| 3/09 | Tue | Final Review |
| 3/11 | Thu | Final |
Homeworks
See CANVAS for the link to assignments. Homework is due and assigned on the dates below.
| Date | Assigned | Due |
|---|---|---|
| 1/19 | HW 1: Image Processing | |
| 1/28 | HW 2: Image Enhancement | HW 1: Image Processing |
| 2/09 | HW 3: Image Restoration | HW 2: Image Enhancement |
| 2/18 | HW 4: Edge Processing | HW3: Image Restoration |
| 3/02 | HW 5: Hough Transform | HW 4: Edge Processing |
| 3/11 | HW 5: Hough Transform |
Credits
Course lectures and materials are adapted from the course EE 5374/7374 – Digital Image Processing, offered by Southern Methodist University. Thanks and acknowledgements to Prof. Prasanna Rangarajan for sharing materials from this course.
