CSE 5524
Transcript Abbreviation:
Computer Vision
Course Description:
Computer vision algorithms for use in human-computer interactive systems; image formation, image features, segmentation, shape analysis, object tracking, motion calculation, and applications.
Course Levels:
Undergraduate (1000-5000 level)
Graduate
Designation:
Elective
General Education Course
(N/A)
Cross-Listings
(N/A)
Credit Hours (Minimum if “Range”selected):
3.00
Max Credit Hours
(N/A)
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: 2331, or Sr or Grad standing.
Electronically Enforced:
No
Exclusions:
Not open to students with credit for 634.
Course Goals / Objectives:
Master fundamental computer vision algorithms
Be competent with computer vision application design and evaluation
Be familiar with Matlab programming environment
Be exposed to original research and applications in computer vision
Check if concurrence sought:
No
Contact Hours:
| Topic | LEC | REC | LAB | LAB Inst |
|---|---|---|---|---|
| Introductory computer vision | 2.0 | 0.0 | 0.0 | 0 |
| Image formation | 2.0 | 0.0 | 0.0 | 0 |
| Noise removal | 2.0 | 0.0 | 0.0 | 0 |
| Edge detection | 2.0 | 0.0 | 0.0 | 0 |
| Pyramids | 1.0 | 0.0 | 0.0 | 0 |
| Region segmentation | 2.0 | 0.0 | 0.0 | 0 |
| 2-D shape | 2.0 | 0.0 | 0.0 | 0 |
| Template matching | 2.0 | 0.0 | 0.0 | 0 |
| Motion | 4.0 | 0.0 | 0.0 | 0 |
| Tracking | 4.0 | 0.0 | 0.0 | 0 |
| 3-D | 2.0 | 0.0 | 0.0 | 0 |
| Event analysis | 4.0 | 0.0 | 0.0 | 0 |
| Features | 2.0 | 0.0 | 0.0 | 0 |
| Stereo | 1.0 | 0.0 | 0.0 | 0 |
| Clustering | 1.0 | 0.0 | 0.0 | 0 |
| Applications | 2.0 | 0.0 | 0.0 | 0 |
| Motion capture | 4.0 | 0.0 | 0.0 | 0 |
| Current research | 2.0 | 0.0 | 0.0 | 0 |
| Total | 41 | 0 | 0 | 0 |
Grading Plan:
Letter Grade
Course Components:
Lecture
Grade Roster Component:
Lecture
Credit by Exam (EM):
No
Grades Breakdown:
| Aspect | Percent |
|---|---|
| Homework assignments | 40% |
| Exam | 20% |
| Project | 30% |
| Participation | 10% |
Representative Textbooks and Other Course Materials:
| Title | Author | Year |
|---|---|---|
| Computer Vision | Shapiro and Stockman |
ABET-CAC Criterion 3 Outcomes:
| Outcome | Contribution | Description |
|---|---|---|
| 1 | Significant contribution (7+ hours) | Analyze a complex computing problem and to apply principles of computing and other relevant disciplines to identify solutions. |
| 2 | Substantial contribution (3-6 hours) | Design, implement, and evaluate a computing-based solution to meet a given set of computing requirements in the context of the program’s discipline. |
| 3 | Some contribution (1-2 hours) | Communicate effectively in a variety of professional contexts. |
| 4 | Some contribution (1-2 hours) | Recognize professional responsibilities and make informed judgments in computing practice based on legal and ethical principles |
| 6 | Substantial contribution (3-6 hours) | Apply computer science theory and software development fundamentals to produce computing-based solutions. |
ABET-ETAC Criterion 3 Outcomes
(N/A)
ABET-EAC Criterion 3 Outcomes:
| Outcome | Contribution | Description |
|---|---|---|
| 1 | Significant contribution (7+ hours) | an ability to identify, formulate, and solve complex engineering problems by applying principles of engineering, science, and mathematics |
| 2 | Substantial contribution (3-6 hours) | an ability to apply engineering design to produce solutions that meet specified needs with consideration of public health, safety, and welfare, as well as global, cultural, social, environmental, and economic factors |
| 3 | Some contribution (1-2 hours) | an ability to communicate effectively with a range of audiences - pre-2019 EAC SLO (g) |
| 4 | Some contribution (1-2 hours) | an ability to recognize ethical and professional responsibilities in engineering situations and make informed judgments, which must consider the impact of engineering solutions in global, economic, environmental, and societal contexts |
| 6 | Significant contribution (7+ hours) | an ability to develop and conduct appropriate experimentation, analyze and interpret data, and use engineering judgment to draw conclusions |
| 7 | Some contribution (1-2 hours) | an ability to acquire and apply new knowledge as needed, using appropriate learning strategies |
Embedded Literacies Info
(N/A)
Attachments
(N/A)
Additional Notes or Comments
(N/A)