TÜRKÇE
ENGLISH
Information
| Code | CEN403 |
| Name | Digital Image Processing |
| Term | 2022-2023 Academic Year |
| Semester | 7. Semester |
| Duration (T+A) | 3-0 (T-A) (17 Week) |
| ECTS | 6 ECTS |
| National Credit | 3 National Credit |
| Teaching Language | İngilizce |
| Level | Lisans Dersi |
| Type | Normal |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Doç. Dr. MUSTAFA ORAL |
| Course Instructor |
1 |
Course Goal / Objective
Computer vision is needed in Industrial automation systems constantly. Especially applications such as piece counting and quality controls are done with computer vision . The aim of this course, to provide manipulation of images and carry out a computer vision software for an industrial application .
Course Content
Mathematical Image Presentations, Image Sampling, Image Exchanges: Fourier, Karhunen-Loeve, etc.., Image quality enhancement: Statistical Methods, Ad Hoc Techniques, Image Restoration: Inverse Filtering, statistical and algebraic.
Course Precondition
None
Resources
1. GONZALEZ R.C., WOODS R.E., and ADDINS S.L., Digital Image Processing Using Matlab, Pearson Education Inc., New Jersey, 2004.
Notes
1. LOW A., Introductory Computer Vision and Image Processing, McGrow-Hill, 1991, ENGLAND. 2. AWCOCK G.J. and THOMAS R., Applied Image Processing, McGrow-Hill, Inc., 1996. 4. 3. JAHNE B., Digital Image Processing, Springer-Verlag, 2005, Netherlands.
Course Learning Outcomes
| Order | Course Learning Outcomes |
|---|---|
| LO01 | Identify the hardware components of computer vision. |
| LO02 | To have knowledge about image processing. |
| LO03 | Create image processing algorithms and write programs |
| LO04 | To design an industrial vision system. |
Relation with Program Learning Outcome
| Order | Type | Program Learning Outcomes | Level |
|---|---|---|---|
| PLO01 | Bilgi - Kuramsal, Olgusal | Has capability in the fields of mathematics, science and computer that form the foundations of engineering | 4 |
| PLO02 | Bilgi - Kuramsal, Olgusal | Identifies, formulates, and solves engineering problems, selects and applies appropriate analytical methods and modeling techniques, | 3 |
| PLO03 | Bilgi - Kuramsal, Olgusal | Analyzes a system, its component, or process and designs under realistic constraints to meet the desired requirements,gains the ability to apply the methods of modern design accordingly. | 3 |
| PLO04 | Bilgi - Kuramsal, Olgusal | Ability to use modern techniques and tools necessary for engineering practice and information technologies effectively. | 3 |
| PLO05 | Bilgi - Kuramsal, Olgusal | Ability to design and to conduct experiments, to collect data, to analyze and to interpret results | 4 |
| PLO06 | Bilgi - Kuramsal, Olgusal | Has ability to work effectively as an individual and in multi-disciplinary teams, take sresponsibility and builds self-confidence | 4 |
| PLO07 | Beceriler - Bilişsel, Uygulamalı | Can access information,gains the ability to do resource research and uses information resources | 5 |
| PLO08 | Beceriler - Bilişsel, Uygulamalı | Awareness of the requirement of lifelong learning, to follow developments in science and technology and continuous self-renewal ability | 2 |
| PLO09 | Yetkinlikler - Bağımsız Çalışabilme ve Sorumluluk Alabilme Yetkinliği | Ability to communicate effectively orally and in writing, and to read and understand technical publications in at least one foreign language | 5 |
| PLO10 | Yetkinlikler - Öğrenme Yetkinliği | Professional and ethical responsibility, | 1 |
| PLO11 | Yetkinlikler - Öğrenme Yetkinliği | Awareness about project management, workplace practices, employee health, environmental and occupational safety, and the legal implications of engineering applications, | |
| PLO12 | Yetkinlikler - Öğrenme Yetkinliği | Becomes aware of universal and social effects of engineering solutions and applications, entrepreneurship and innovation, and knowledge of contemporary issues |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Hardware and Software Structure f oComputer Vision System | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 2 | Image Matrix, the Principles of Neighborhood | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 3 | Gray-Level Image Processing, Binary Image Processing, Color-Image Processing, Differences and Usages | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 4 | Quantization, Thresholding, Histogram, Noise Reduction Techniques | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 5 | Edge Detection, Corner Detection | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 6 | Image Analysis for Pattern Recognition | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 7 | Pixel-Based Operations | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 8 | Mid-Term Exam | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
| 9 | Morphological Operations | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 10 | Image Compression | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma |
| 11 | Sample Applications for 1st category- Presentations | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma, Bireysel Çalışma, Proje Temelli Öğrenme , Soru-Cevap |
| 12 | Sample Applications 2nd category - Presentations | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Tartışma, Bireysel Çalışma, Proje Temelli Öğrenme , Soru-Cevap |
| 13 | Sample Applications for 3rd category - Presentations | Reading the lecture notes | Öğretim Yöntemleri: Tartışma, Soru-Cevap, Bireysel Çalışma, Proje Temelli Öğrenme |
| 14 | Sample Applications for 4th category - Presentations | Reading the lecture notes | Öğretim Yöntemleri: Tartışma, Alıştırma ve Uygulama |
| 15 | Sample Applications for 5th category- Presentations | Reading the lecture notes | Öğretim Yöntemleri: Anlatım, Soru-Cevap, Bireysel Çalışma, Proje Temelli Öğrenme |
| 16 | Term Exams | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
| 17 | Term Exams | Exam preparation | Ölçme Yöntemleri: Yazılı Sınav |
Student Workload - ECTS
| Works | Number | Time (Hour) | Workload (Hour) |
|---|---|---|---|
| Course Related Works | |||
| Class Time (Exam weeks are excluded) | 14 | 3 | 42 |
| Out of Class Study (Preliminary Work, Practice) | 14 | 5 | 70 |
| Assesment Related Works | |||
| Homeworks, Projects, Others | 0 | 0 | 0 |
| Mid-term Exams (Written, Oral, etc.) | 1 | 15 | 15 |
| Final Exam | 1 | 30 | 30 |
| Total Workload (Hour) | 157 | ||
| Total Workload / 25 (h) | 6,28 | ||
| ECTS | 6 ECTS | ||