ECE 7866
Transcript Abbreviation:
Computer Vision
Course Description:
Computer vision systems, image models, feature extraction, shape representation and recognition, object modeling and recognition, matching, probabilistic and statistical modeling, semantic knowledge, and face perception.
Course Levels:
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: 5460 (707).
Electronically Enforced:
No
Exclusions:
Not open to students with credit for 863.
Course Goals / Objectives:
Teach the fundamental concepts in computer vision and to prepare the student to design simple vision systems, to read the literature, and to commence a research program in computer vision should he or she so desire
Topics include structure from motion, image segmentation, feature extraction, shape representation, object modeling and recognition, matching, and faces
Practice in computer vision concepts and system design is provided by a term project, conducted in teams and drawn from real-world applications
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 |
|---|---|---|---|---|---|
| Introduction to computer vision | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Imaging geometry | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Image segmentation | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Object modeling and recognition | 9.0 | 0.0 | 0 | 0.0 | 0 |
| Probabilistic approaches | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Face processing | 6.0 | 0.0 | 0 | 0.0 | 0 |
| Motion analysis | 3.0 | 0.0 | 0 | 0.0 | 0 |
| Total | 39 | 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 and/or midterms | 25% |
| Individual projects | 25% |
| Final project and/or final exam | 50% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| No Textbooks and Other Course Materials Entered. | ||
ABET-CAC Criterion 3 Outcomes:
(N/A)
ABET-ETAC Criterion 3 Outcomes:
(N/A)
ABET-EAC Criterion 3 Outcomes:
(N/A)
Embedded Literacies Info: