Course Detail
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|
| INTRODUCTION to COMPUTER VISION | EEE3147020 | Fall Semester | 3+0 | 3 | 6 |
| Prerequisites Courses | |
| Recommended Elective Courses | |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Prof.Dr. Bahadır Kürşat GÜNTÜRK |
| Name of Lecturer(s) | Assist.Prof. İbrahim KARLIAĞA |
| Assistant(s) | |
| Aim | To understand the basic topics in computer vision and to apply and evaluate various computer vision techniques. |
| Course Content | This course contains; Optical image formation,Imaging pipeline,Image filtering,Edge detection and Hough transform,Morphological operations,Image enhancement,Keypoint detection (basic ideas),Keypoint detection (scale invariant methods),Image interpolation,Geometric transformations,Motion estimation,Camera calibration,3D vision,Color space. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Understand and apply basic image processing techniques | 12, 14, 6, 9 | A, E |
| Understand and apply image formation and modeling concepts | 12, 14, 16, 6, 9 | A, E |
| Understand and apply mid-level computer vision techniques, including feature extraction and optical flow | 12, 14, 16, 6, 9 | A, E |
| Design and evaluate solutions to computer vision problems | 12, 14, 16, 6, 9 | A, E |
| Teaching Methods: | 12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework |
Course Outline
| Order | Subjects | Preliminary Work |
|---|
| 1 | Optical image formation | |
| 2 | Imaging pipeline | |
| 3 | Image filtering | |
| 4 | Edge detection and Hough transform | |
| 5 | Morphological operations | |
| 6 | Image enhancement | |
| 7 | Keypoint detection (basic ideas) | |
| 8 | Keypoint detection (scale invariant methods) | |
| 9 | Image interpolation | |
| 10 | Geometric transformations | |
| 11 | Motion estimation | |
| 12 | Camera calibration | |
| 13 | 3D vision | |
| 14 | Color space | |
| Resources |
| Sonka, Hlavac, and Boyle. “Image Processing, Analysis, and Machine Vision.” Cengage Learning, 4th edition. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications |
| No | Program Qualification | Contribution Level |
| 1 | 2 | 3 | 4 | 5 |
| 1 | An ability to apply knowledge of mathematics, science, and engineering | | | | | X |
| 2 | An ability to identify, formulate, and solve engineering problems | | | | | X |
| 3 | An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability | | | X | | |
| 4 | An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice | | | | | X |
| 5 | An ability to design and conduct experiments, as well as to analyze and interpret data | | | X | | |
| 6 | An ability to function on multidisciplinary teams | | | X | | |
| 7 | An ability to communicate effectively | X | | | | |
| 8 | A recognition of the need for, and an ability to engage in life-long learning | | | | | |
| 9 | An understanding of professional and ethical responsibility | | | | | |
| 10 | A knowledge of contemporary issues | | | | | |
| 11 | The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context | | | | | |
Assessment Methods
| Contribution Level | Absolute Evaluation |
| Rate of Midterm Exam to Success | | 30 |
| Rate of Final Exam to Success | | 70 |
| Total | | 100 |
| ECTS / Workload Table |
| Activities | Number of | Duration(Hour) | Total Workload(Hour) |
| Course Hours | 14 | 3 | 42 |
| Guided Problem Solving | 0 | 0 | 0 |
| Resolution of Homework Problems and Submission as a Report | 1 | 30 | 30 |
| Term Project | 0 | 0 | 0 |
| Presentation of Project / Seminar | 0 | 0 | 0 |
| Quiz | 0 | 0 | 0 |
| Midterm Exam | 1 | 32 | 32 |
| General Exam | 1 | 32 | 32 |
| Performance Task, Maintenance Plan | 0 | 0 | 0 |
| Total Workload(Hour) | 136 |
| Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(136/30) | 5 |
| ECTS of the course: 30 hours of work is counted as 1 ECTS credit. |
Detail Informations of the Course
Course Description
| Course | Code | Semester | T+P (Hour) | Credit | ECTS |
|---|
| INTRODUCTION to COMPUTER VISION | EEE3147020 | Fall Semester | 3+0 | 3 | 6 |
| Prerequisites Courses | |
| Recommended Elective Courses | |
| Language of Course | English |
| Course Level | First Cycle (Bachelor's Degree) |
| Course Type | Elective |
| Course Coordinator | Prof.Dr. Bahadır Kürşat GÜNTÜRK |
| Name of Lecturer(s) | Assist.Prof. İbrahim KARLIAĞA |
| Assistant(s) | |
| Aim | To understand the basic topics in computer vision and to apply and evaluate various computer vision techniques. |
| Course Content | This course contains; Optical image formation,Imaging pipeline,Image filtering,Edge detection and Hough transform,Morphological operations,Image enhancement,Keypoint detection (basic ideas),Keypoint detection (scale invariant methods),Image interpolation,Geometric transformations,Motion estimation,Camera calibration,3D vision,Color space. |
| Dersin Öğrenme Kazanımları | Teaching Methods | Assessment Methods |
| Understand and apply basic image processing techniques | 12, 14, 6, 9 | A, E |
| Understand and apply image formation and modeling concepts | 12, 14, 16, 6, 9 | A, E |
| Understand and apply mid-level computer vision techniques, including feature extraction and optical flow | 12, 14, 16, 6, 9 | A, E |
| Design and evaluate solutions to computer vision problems | 12, 14, 16, 6, 9 | A, E |
| Teaching Methods: | 12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 6: Experiential Learning, 9: Lecture Method |
| Assessment Methods: | A: Traditional Written Exam, E: Homework |
Course Outline
| Order | Subjects | Preliminary Work |
|---|
| 1 | Optical image formation | |
| 2 | Imaging pipeline | |
| 3 | Image filtering | |
| 4 | Edge detection and Hough transform | |
| 5 | Morphological operations | |
| 6 | Image enhancement | |
| 7 | Keypoint detection (basic ideas) | |
| 8 | Keypoint detection (scale invariant methods) | |
| 9 | Image interpolation | |
| 10 | Geometric transformations | |
| 11 | Motion estimation | |
| 12 | Camera calibration | |
| 13 | 3D vision | |
| 14 | Color space | |
| Resources |
| Sonka, Hlavac, and Boyle. “Image Processing, Analysis, and Machine Vision.” Cengage Learning, 4th edition. |
Course Contribution to Program Qualifications
| Course Contribution to Program Qualifications |
| No | Program Qualification | Contribution Level |
| 1 | 2 | 3 | 4 | 5 |
| 1 | An ability to apply knowledge of mathematics, science, and engineering | | | | | X |
| 2 | An ability to identify, formulate, and solve engineering problems | | | | | X |
| 3 | An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability | | | X | | |
| 4 | An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice | | | | | X |
| 5 | An ability to design and conduct experiments, as well as to analyze and interpret data | | | X | | |
| 6 | An ability to function on multidisciplinary teams | | | X | | |
| 7 | An ability to communicate effectively | X | | | | |
| 8 | A recognition of the need for, and an ability to engage in life-long learning | | | | | |
| 9 | An understanding of professional and ethical responsibility | | | | | |
| 10 | A knowledge of contemporary issues | | | | | |
| 11 | The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context | | | | | |
Assessment Methods
| Contribution Level | Absolute Evaluation |
| Rate of Midterm Exam to Success | | 30 |
| Rate of Final Exam to Success | | 70 |
| Total | | 100 |
Numerical Data
Ekleme Tarihi: 09/10/2023 - 10:37Son Güncelleme Tarihi: 09/10/2023 - 10:37
×- A-Z Programs
- Undergraduate
- Graduate
- Academic Calendar
- Double Major & Minor Programs
- Erasmus
- Prospective Students
- Registration
- Re-Enrolment
- Fees
- Directorate of Registrar’s Office
- FAQ
- Accommodation
- Scholarships
- Lateral and Vertical Transfer
- Summer School
- Preparation
- Transportation
