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 | Natural & Applied Sciences Master's Degree |
| 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 | Accumulated knowledge on mathematics, science and mechatronics engineering; an ability to apply the theoretical and applied knowledge of mathematics, science and mechatronics engineering to model and analyze mechatronics engineering problems. | |||||
| 2 | An ability to differentiate, identify, formulate, and solve complex engineering problems; an ability to select and implement proper analysis, modeling and implementation techniques for the identified engineering problems. | |||||
| 3 | An ability to design a complex system, product, component or process to meet the requirements under realistic constraints and conditions; an ability to apply contemporary design methodologies; an ability to implement effective engineering creativity techniques in mechatronics engineering. (Realistic constraints and conditions may include economics, environment, sustainability, producibility, ethics, human health, social and political problems.) | |||||
| 4 | An ability to develop, select and use modern techniques, skills and tools for application of mechatronics engineering and robot technologies; an ability to use information and communications technologies effectively. | |||||
| 5 | An ability to design experiments, perform experiments, collect and analyze data and assess the results for investigated problems on mechatronics engineering and robot technologies. | |||||
| 6 | An ability to work effectively on single disciplinary and multi-disciplinary teams; an ability for individual work; ability to communicate and collaborate/cooperate effectively with other disciplines and scientific/engineering domains or working areas, ability to work with other disciplines. | |||||
| 7 | An ability to express creative and original concepts and ideas effectively in Turkish and English language, oral and written. | |||||
| 8 | An ability to reach information on different subjects required by the wide spectrum of applications of mechatronics engineering, criticize, assess and improve the knowledge-base; consciousness on the necessity of improvement and sustainability as a result of life-long learning; monitoring the developments on science and technology; awareness on entrepreneurship, innovative and sustainable development and ability for continuous renovation. | |||||
| 9 | Be conscious on professional and ethical responsibility, competency on improving professional consciousness and contributing to the improvement of profession itself. | |||||
| 10 | A knowledge on the applications at business life such as project management, risk management and change management and competency on planning, managing and leadership activities on the development of capabilities of workers who are under his/her responsibility working around a project. | |||||
| 11 | Knowledge about the global, societal and individual effects of mechatronics engineering applications on the human health, environment and security and cultural values and problems of the era; consciousness on these issues; awareness of legal results of engineering solutions. | |||||
| 12 | Competency on defining, analyzing and surveying databases and other sources, proposing solutions based on research work and scientific results and communicate and publish numerical and conceptual solutions. | |||||
| 13 | Consciousness on the environment and social responsibility, competencies on observation, improvement and modify and implementation of projects for the society and social relations and be an individual within the society in such a way that planing, improving or changing the norms with a criticism | |||||
| 14 | A competency on developing strategy, policy and application plans on the mechatronics engineering and evaluating the results in the context of qualitative processes. | |||||
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 | ||