Manufacturing Execution Systems Operator
Course Details
KTO KARATAY UNIVERSITY
Trade and Industry Vocational School
Programme of Manufacturing Execution Systems Operator
Course Details
Trade and Industry Vocational School
Programme of Manufacturing Execution Systems Operator
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 07831116 | Image Processing Techniques | 2 | Autumn | 3 | 2+2+0 | 5 | 5 |
| Course Type | Elective |
| Course Cycle | Associate (Short Cycle) (TQF-HE: Level 5 / QF-EHEA: Short Cycle / EQF-LLL: Level 5) |
| Course Language | Turkish |
| Methods and Techniques | Lectures, laboratory applications, visual data analysis, simulation studies, and project-based learning methods are employed. |
| Mode of Delivery | Face to Face |
| Prerequisites | None; basic programming knowledge is recommended. |
| Coordinator | Lect. Mehmet AKSOY |
| Instructor(s) | Lect. Yasin BÜYÜKER |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Lect. Yasin BÜYÜKER | T-219 | [email protected] | 7408 | Tuesday 14:00/16:00 |
Course Content
The course covers the concept of images, digitization and pixel-based representation methods, color spaces, filtering and enhancement techniques, edge detection and segmentation, morphological operations, object recognition, and feature extraction. Analysis and classification of image data and its integration into decision support systems are explained through practical examples.
Objectives of the Course
The aim of this course is to enable students to understand the processes of digital image processing, analysis, and interpretation. Students learn the fundamentals of image processing and gain the ability to design solutions applicable to quality control, defect detection, measurement, and automation in manufacturing systems.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | |
| Support Courses | |
| Transferable Skills Courses | |
| Humanities, Communication and Management Skills Courses |
Weekly Detailed Course Contents
| Week | Topics |
|---|---|
| 1 | Introduction to image processing concepts and basic definitions |
| 2 | Digital image representation and pixel structure |
| 3 | Color models and grayscale conversions |
| 4 | Image filtering and noise reduction techniques |
| 5 | Image enhancement and contrast improvement methods |
| 6 | Edge detection and contour analysis |
| 7 | Segmentation methods and region-based algorithms |
| 8 | Morphological operations: dilation, erosion, opening, and closing |
| 9 | Feature extraction and basics of object recognition |
| 10 | Shape analysis, dimension measurement, and geometric transformations |
| 11 | Industrial image processing applications: defect detection and quality control |
| 12 | Motion analysis and object tracking |
| 13 | Classification of image data and integration into decision support systems |
| 14 | Intelligent image processing systems and visual analytics in manufacturing |
Textbook or Material
| Resources | Digital Image Processing, Gonzalez & Woods |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Quiz | - | - |
| Midterms | - | - |
| Final Exam | - | - |
| Total | 0 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 0 | 0 | 0 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 0 | 0 | 0 |
| Midterms | 0 | 0 | 0 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| 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 | 0 | 0 | 0 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 0 | ||
| Total Work Load / 30 | 0 | ||
| Course ECTS Credits: | 0 | ||