ECTS - Computer Vision
Computer Vision (EE573) Course Detail
| Course Name | Course Code | Season | Lecture Hours | Application Hours | Lab Hours | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| Computer Vision | EE573 | 3 | 0 | 0 | 3 | 5 |
| Pre-requisite Course(s) |
|---|
| Math 158, Math 275, EE 204 |
| Course Language | English |
|---|---|
| Course Type | N/A |
| Course Level | Ph.D. |
| Mode of Delivery | Face To Face |
| Learning and Teaching Strategies | Lecture, Drill and Practice. |
| Course Lecturer(s) |
|
| Course Objectives | • Study the fundamental problems of computer vision • Study the fundamental concepts and techniques used to solve problems in computer vision • Study typical application domains where computer vision and video electronics are used |
| Course Learning Outcomes |
The students who succeeded in this course;
|
| Course Content | Human vision, geometric camera models, image segmentation, object recognition, video signals and standards, vision system design, computer vision and digital video applications. |
Weekly Subjects and Releated Preparation Studies
| Week | Subjects | Preparation |
|---|---|---|
| 1 | Introduction: Fundamentals of imaging, The Physics of Imaging | Glance this week’s topics from the lecture |
| 2 | Images and Imaging Operations: Image processing operations and image filtering operations | Glance this week’s topics from the lecture |
| 3 | Images and Imaging Operations | Review last week and glance this week’s topics from the lecture |
| 4 | Image Segmentation: Clustering methods, fitting a model | Glance this week’s topics from the lecture |
| 5 | Image Segmentation | Review last week and glance this week’s topics from the lecture |
| 6 | Introduction to Recognition: Model of pattern classification, statistical techniques for classification | Glance this week’s topics from the lecture |
| 7 | Introduction to Recognition | Review last week and glance this week’s topics from the lecture |
| 8 | Geometric Camera Models: Camera parameters and the perspective projection, affine cameras, camera calibration | Glance this week’s topics from the lecture |
| 9 | Geometric Camera Models | Review last week and glance this week’s topics from the lecture |
| 10 | Video Signals and Standards: Introduction to digital video, image and video compression and decompression | Glance this week’s topics from the lecture |
| 11 | Video Signals and Standards | Review last week and glance this week’s topics from the lecture |
| 12 | Vision System Design: Cameras and Digitization, Real time hardware and systems design considerations , Basic ideas on optimal hardware implementations | Glance this week’s topics from the lecture |
| 13 | Applications: Automated visual inspection, biometrics, robotics, people tracking, video surveillance, human-computer interaction | Glance this week’s topics from the lecture |
| 14 | Applications | Review last week and glance this week’s topics from the lecture |
| 15 | Final Examination period | Review of topics |
| 16 | Final Examination period | Review of topics |
Sources
| Course Book | 1. Computer Vision: A Modern Approach, David A. Forsyth and Jean Ponce, Prentice Hall, 2003 |
|---|---|
| Other Sources | 2. Machine vision: theory, algorithms, practicalities, Davies, E. R. (E. Roy), Elsevier, 2005 |
Evaluation System
| Requirements | Number | Percentage of Grade |
|---|---|---|
| Attendance/Participation | - | - |
| Laboratory | - | - |
| Application | 8 | 15 |
| Field Work | - | - |
| Special Course Internship | - | - |
| Quizzes/Studio Critics | - | - |
| Homework Assignments | 15 | 10 |
| Presentation | - | - |
| Project | 1 | 25 |
| Report | - | - |
| Seminar | - | - |
| Midterms Exams/Midterms Jury | 1 | 20 |
| Final Exam/Final Jury | 1 | 30 |
| Toplam | 26 | 100 |
| Percentage of Semester Work | 70 |
|---|---|
| Percentage of Final Work | 30 |
| Total | 100 |
Course Category
| Core Courses | X |
|---|---|
| Major Area Courses | |
| Supportive Courses | |
| Media and Managment Skills Courses | |
| Transferable Skill Courses |
The Relation Between Course Learning Competencies and Program Qualifications
| # | Program Qualifications / Competencies | Level of Contribution | ||||
|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | ||
| 1 | Ability to carry out advanced research activities, both individual and as a member of a team | |||||
| 2 | Ability to evaluate research topics and comment with scientific reasoning | |||||
| 3 | Ability to initiate and create new methodologies, implement them on novel research areas and topics | |||||
| 4 | Ability to produce experimental and/or analytical data in systematic manner, discuss and evaluate data to lead scintific conclusions | |||||
| 5 | Ability to apply scientific philosophy on analysis, modelling and design of engineering systems | |||||
| 6 | Ability to synthesis available knowledge on his/her domain to initiate, to carry, complete and present novel research at international level | |||||
| 7 | Contribute scientific and technological advancements on engineering domain of his/her interest area | |||||
| 8 | Contribute industrial and scientific advancements to improve the society through research activities | |||||
ECTS/Workload Table
| Activities | Number | Duration (Hours) | Total Workload |
|---|---|---|---|
| Course Hours (Including Exam Week: 16 x Total Hours) | 16 | 3 | 48 |
| Laboratory | |||
| Application | |||
| Special Course Internship | |||
| Field Work | |||
| Study Hours Out of Class | 16 | 3 | 48 |
| Presentation/Seminar Prepration | 1 | 4 | 4 |
| Project | 5 | 3 | 15 |
| Report | |||
| Homework Assignments | 5 | 2 | 10 |
| Quizzes/Studio Critics | |||
| Prepration of Midterm Exams/Midterm Jury | 2 | 2 | 4 |
| Prepration of Final Exams/Final Jury | 1 | 2 | 2 |
| Total Workload | 131 | ||