ECE 5460
Transcript Abbreviation:
Image Processing
Course Description:
Fundamentals and research directions in image processing: cameras, geometry, calibration, 2D and 3D image reconstruction, stereo, structure from motion, Radiometry, filtering, motion estimation, and applications.
Course Levels:
Undergraduate (1000-5000 level)
Graduate (5000-8000 level)
Designation:
Elective
General Education Course:
(N/A)
Cross-Listings:
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours:
3.00
Select if Repeatable:
Off
Maximum Repeatable Credits:
(N/A)
Total Completions Allowed:
(N/A)
Allow Multiple Enrollments in Term:
No
Course Length:
14 weeks (autumn or spring)
12 weeks (summer only)
Off Campus:
Never
Campus Location:
Columbus
Instruction Modes:
In Person (75-100% campus; 0-24% online)
Prerequisites and Co-requisites:
Prereq: 5200 (600), and Stat 3470 (427) or Math 530; or Grad standing in Engineering, Biological Sciences, Statistics, Bioinformatics, or Math and Physical Sciences.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for 707.
Course Goals / Objectives:
Learn the mathematical underpinnings of digital image processing
Learn to design systems for image restoration, 3D model reconstruction, how to interpret and modify the radiometry parameters of an image, image filtering, and motion analysis
Learn to develop image processing solutions through computer projects and real image experiments, documented with written reports
Develop an image processing system to meet a broad set of design specifications, working in teams, and presenting the results in formal reports
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC out-of-class | REC in-class | Weekly LAB out-of-class | Weekly LAB in-class |
|---|---|---|---|---|---|
| Camera Models | 4.5 | 0.0 | 0 | 0.0 | 0 |
| Camera Calibration | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Geometry of Multiple Views and Stereo | 4.5 | 0.0 | 0 | 0.0 | 0 |
| Structure from Motion | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Radiometry, Shadows and Shading | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Introduction to Optical Flow and Segmentation | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Linear filters, edge detection and compression | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Computer projects | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Total | 36 | 0 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework | 25% |
| Individual projects and/or midterm exams and quizzes | 35% |
| Final project and/or final exam | 40% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Computer Vision: A Modern Approach | Forsyth and Ponce |
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
(N/A)
Embedded Literacies Info: