Information Security Technology
Course Details
KTO KARATAY UNIVERSITY
Trade and Industry Vocational School
Programme of Information Security Technology
Course Details
Trade and Industry Vocational School
Programme of Information Security Technology
Course Details
| Course Code | Course Name | Year | Period | Semester | T+A+L | Credit | ECTS |
|---|---|---|---|---|---|---|---|
| 08131101 | Introduction to Image Processing | 2025 | 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 | - |
| Mode of Delivery | Face to Face |
| Prerequisites | - |
| Coordinator | - |
| Instructor(s) | Lect. Ayşe Merve BÜYÜKBAŞ |
| Instructor Assistant(s) | - |
Course Instructor(s)
| Name and Surname | Room | E-Mail Address | Internal | Meeting Hours |
|---|---|---|---|---|
| Lect. Ayşe Merve BÜYÜKBAŞ | C-127 | [email protected] | 7436 | Wednesday 10:00-12:00 |
Course Content
This course includes the topics of Introduction to image processing, Fundamentals of image processing, Image enhancement in position domain, Image enhancement in frequency domain, Image restoration, Color image processing, Wavelet transform and multi-resolution image processing, Image compression, Image processing methods, Application in Matlab and Project application.
Objectives of the Course
This course aims to provide students with basic knowledge and application skills in the processes of processing, enhancing, compressing and analyzing digital images, and to practice by applying image processing methods in the Matlab environment.
Contribution of the Course to Field Teaching
| Basic Vocational Courses | |
| Specialization / Field Courses | |
| Support Courses | X |
| 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 |
|---|---|---|
| P1 | He/she has basic, current and practical knowledge about his/her profession. | 2 |
| P3 | Follows current developments and practices for his/her profession and uses them effectively. | 5 |
| P4 | Uses professionally relevant information technologies (software, programs, animations, etc.) effectively. | 4 |
| P6 | Can effectively present thoughts through written and verbal communication at the level of knowledge and skills and express them in an understandable manner. | 3 |
| P14 | Performs mathematical calculations. | 4 |
Course Learning Outcomes
| Upon the successful completion of this course, students will be able to: | |||
|---|---|---|---|
| No | Learning Outcomes | Outcome Relationship | Measurement Method ** |
| O1 | Demonstrates the learned information with practical examples. | P.1.6 | 2,3,7 |
| O2 | Applies current software tools. | P.3.2 | 1,2,7 |
| O3 | Defines programming environments. | P.4.2 | 1,2,7 |
| O4 | Can use software appropriate for current mathematical modeling and optimization methods. | P.4.6 | 1,2 |
| O5 | Uses mathematical knowledge to summarize the obtained data. | P.14.3 | 1,2,3,7 |
| O6 | Can report computer and data science analyses and their results | P.6.3 | 1,2 |
| ** 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 image processing |
| 2 | Fundamentals of image processing |
| 3 | Image enhancement in the spatial domain |
| 4 | Image enhancement in the frequency domain |
| 5 | Image restoration |
| 6 | Color image processing |
| 7 | Wavelet transform and multiresolution image processing |
| 8 | Midterm Exam |
| 9 | Image compression |
| 10 | Image processing methods |
| 11 | Image processing methods |
| 12 | Application in Matlab |
| 13 | Application in Matlab |
| 14 | Project implementation |
| 15 | Final Exam |
Textbook or Material
| Resources | Notes shared by the course instructor |
| Digital image processing, Gonzalez and Woods, Copyright 2002. |
Evaluation Method and Passing Criteria
| In-Term Studies | Quantity | Percentage |
|---|---|---|
| Attendance | - | - |
| Laboratory | - | - |
| Practice | - | - |
| Field Study | - | - |
| Course Specific Internship (If Any) | - | - |
| Homework | - | - |
| Presentation | - | - |
| Projects | - | - |
| Seminar | - | - |
| Quiz | 1 | 5 (%) |
| Listening | - | - |
| Midterms | 1 | 35 (%) |
| Final Exam | 1 | 60 (%) |
| Total | 100 (%) | |
ECTS / Working Load Table
| Quantity | Duration | Total Work Load | |
|---|---|---|---|
| Course Week Number and Time | 14 | 8 | 112 |
| Out-of-Class Study Time (Pre-study, Library, Reinforcement) | 1 | 14 | 14 |
| Midterms | 1 | 10 | 10 |
| Quiz | 0 | 0 | 0 |
| Homework | 0 | 0 | 0 |
| Practice | 1 | 4 | 4 |
| 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 | 10 | 10 |
| Other | 0 | 0 | 0 |
| Total Work Load: | 150 | ||
| Total Work Load / 30 | 5 | ||
| Course ECTS Credits: | 5 | ||
Course - Learning Outcomes Matrix
| Relationship Levels | ||||
| Lowest | Low | Medium | High | Highest |
| 1 | 2 | 3 | 4 | 5 |
| # | Learning Outcomes | P1 | P3 | P4 | P6 | P14 |
|---|---|---|---|---|---|---|
| O1 | Demonstrates the learned information with practical examples. | 4 | 3 | 4 | 2 | 2 |
| O2 | Applies current software tools. | 2 | 5 | 5 | 2 | 2 |
| O3 | Defines programming environments. | 3 | 4 | 5 | 2 | 2 |
| O4 | Can use software appropriate for current mathematical modeling and optimization methods. | 3 | 5 | 4 | 2 | 5 |
| O5 | Can report computer and data science analyses and their results | 3 | 3 | 4 | 5 | 3 |
| O6 | Uses mathematical knowledge to summarize the obtained data. | 2 | 3 | 3 | 3 | 5 |