CEN403 Digital Image Processing

6 ECTS - 3-0 Duration (T+A)- 7. Semester- 3 National Credit

Information

Code CEN403
Name Digital Image Processing
Term 2023-2024 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

Update Time: 09.05.2023 07:10