Mechatronics Engineering Master of Science
Course Details
KTO KARATAY UNIVERSITY
Graduate Education Institute
Programme of Mechatronics Engineering Master of Science
Course Details
Graduate Education Institute
Programme of Mechatronics Engineering Master of Science
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 81811120 | Advanced Robot Vision | 1 | Spring | 2 | 3+0+0 | 7 | 7 |
| Course Type | Elective |
| Course Cycle | - |
| Course Language | Turkish |
| Methods and Techniques | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | Asst. Prof. Amir Yavariabdi |
| Instructor(s) | Asst. Prof. Emre OFLAZ |
| Instructor Assistant(s) | - |
Course Content
Görüntü tipleri ve özellikleri, Görüntü kayıt sistemlerinin özellikleri, görüntünün geometrik olarak düzeltilmesi, Görüntü iyileştirme teknikleri, Lineer diferansiyel denklemler, Görüntü füzyon metotları, Özellik çıkarma ve Eşleştirme Tabanlı Yöntemler, Optik Akış yöntemleri, Denetimli ve denetimsiz sınıflandırma metotları, Yapay sinir ağları ile sınıflandırma, derin öğrenme nesnesi tespiti metotları
Objectives of the Course
1. To study the problems in computer vision
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | X |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Relationships between Course Learning Outcomes and Program Outcomes
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Program Learning Outcomes | Level |
|---|---|---|
| P3 | Temel bilimlerin ve teknolojinin güncel durumuna göre yenilikçi çözümler önerebilmeli | 5 |
| P5 | Ability to do patent searching and literature research. | 5 |
| P7 | Ability to propose innovative solution according to basic science and technological developments. | 5 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Ability to apply algorithms and techniques in the literature to solve low, medium and high level image problems. | P.5.1 | |
| O2 | Acquisition of images using a single or multiple cameras | P.3.13 | |
| O3 | Ability to detect and recognize objects of interest, extract motion content from image sequences, and extract three-dimensional structure information from images. | P.7.3 | |
| O4 | Ability to write programs that can perform image segmentation, image matching and object detection or object recognition. | P.7.4 | |
| ** Written Exam: 1, Oral Exam: 2, Homework: 3, Lab./Exam: 4, Seminar/Presentation: 5, Term Paper: 6, Application: 7 | |||
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Introduction to Robot Vision |
| 2 | Robot Vision View (Image Formation and Perception, Reflections, Brightness, Lenses, Image Perception) |
| 3 | Stereo Images and Their Properties (Basics, Geometric Properties, Topological Properties) |
| 4 | Differential Equations |
| 5 | Discrete Approaches to Continuous Boundary Value Problems |
| 6 | Rigid Image Registration |
| 7 | Deformable Image Registration |
| 8 | Application of Splines in Computer Vision Problems |
| 9 | Feature Extraction and Matching Based Methods |
| 10 | Optical Flow Algorithms |
| 11 | Stereo Vision |
| 12 | Introduction to Deep Learning |
| 13 | Object Detection |
| 14 | Presentations |
Textbook or Material
| Resources | Computer Vision: Algorithms and Applications, Richard Szeliski,Springer-Verlag, 2010. |
| Machine vision: theory, algorithms, practicalities, Davies, E. R. (E. Roy), Elsevier, 2005 |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | 1 | 50 (%) |
| Seminar | - | - |
| Quiz | - | - |
| Midterms | - | - |
| Final Exam | 1 | 50 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 13 | 19 | 247 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 0 | 0 | 0 |
| Midterms | 0 | 0 | 0 |
| Quiz | 0 | 0 | 0 |
| Homework | 1 | 2 | 2 |
| Practice | 0 | 0 | 0 |
| Laboratory | 0 | 0 | 0 |
| Project | 0 | 0 | 0 |
| Workshop | 0 | 0 | 0 |
| Presentation/Seminar Preparation | 0 | 0 | 0 |
| Fieldwork | 0 | 0 | 0 |
| Final Exam | 1 | 2 | 2 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 251 | ||
| Total Work Load / 30 | 8,37 | ||
| Course ECTS Credits: | 8 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P3 | P5 | P7 |
|---|---|---|---|---|
| O1 | Acquisition of images using a single or multiple cameras | 3 | - | - |
| O2 | Ability to apply algorithms and techniques in the literature to solve low, medium and high level image problems. | - | 3 | - |
| O3 | Ability to detect and recognize objects of interest, extract motion content from image sequences, and extract three-dimensional structure information from images. | - | - | 5 |
| O4 | Ability to write programs that can perform image segmentation, image matching and object detection or object recognition. | - | - | 5 |