TÜRKÇE
ENGLISH
Information
| Unit | FACULTY OF ENGINEERING |
| COMPUTER ENGINEERING PR. (ENGLISH) | |
| Code | CEN403 |
| Name | Digital Image Processing |
| Term | 2018-2019 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 |
| Label | E Elective |
| Mode of study | Yüz Yüze Öğretim |
| Catalog Information Coordinator | Doç. Dr. MUSTAFA ORAL |
| Course Instructor |
Doç. Dr. MUSTAFA ORAL
(Güz)
(A Group)
(Ins. in Charge)
|
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
Yok
Resources
Notes
1. GONZALEZ R.C., WOODS R.E., and ADDINS S.L., Digital Image Processing Using Matlab, Pearson Education Inc., New Jersey, 2004. 2. LOW A., Introductory Computer Vision and Image Processing, McGrow-Hill, 1991, ENGLAND. 3. AWCOCK G.J. and THOMAS R., Applied Image Processing, McGrow-Hill, Inc., 1996. 4. 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 | - | Has capability in the fields of mathematics, science and computer that form the foundations of engineering | 5 |
| PLO02 | - | Identifies, formulates, and solves engineering problems, selects and applies appropriate analytical methods and modeling techniques, | 4 |
| PLO03 | - | 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. | 4 |
| PLO04 | - | Ability to use modern techniques and tools necessary for engineering practice and information technologies effectively. | 4 |
| PLO05 | - | Ability to design and to conduct experiments, to collect data, to analyze and to interpret results | 3 |
| PLO06 | - | Has ability to work effectively as an individual and in multi-disciplinary teams, take sresponsibility and builds self-confidence | 3 |
| PLO07 | - | Can access information,gains the ability to do resource research and uses information resources | 4 |
| PLO08 | - | Awareness of the requirement of lifelong learning, to follow developments in science and technology and continuous self-renewal ability | 1 |
| PLO09 | - | Ability to communicate effectively orally and in writing, and to read and understand technical publications in at least one foreign language | 1 |
| PLO10 | - | Professional and ethical responsibility, | 4 |
| PLO11 | - | Awareness about project management, workplace practices, employee health, environmental and occupational safety, and the legal implications of engineering applications, | 4 |
| PLO12 | - | Becomes aware of universal and social effects of engineering solutions and applications, entrepreneurship and innovation, and knowledge of contemporary issues | 1 |
Week Plan
| Week | Topic | Preparation | Methods |
|---|---|---|---|
| 1 | Hardware and Software Structure f oComputer Vision System | Reading the lecture notes | |
| 2 | Image Matrix, the Principles of Neighborhood | Reading the lecture notes | |
| 3 | Gray-Level Image Processing, Binary Image Processing, Color-Image Processing, Differences and Usages | Reading the lecture notes | |
| 4 | Quantization, Thresholding, Histogram, Noise Reduction Techniques | Reading the lecture notes | |
| 5 | Edge Detection, Corner Detection | Reading the lecture notes | |
| 6 | Image Analysis for Pattern Recognition | Reading the lecture notes | |
| 7 | Pixel-Based Operations | Reading the lecture notes | |
| 8 | Mid-Term Exam | Reading the lecture notes | |
| 9 | Morphological Operations | Reading the lecture notes | |
| 10 | Image Compression | Reading the lecture notes | |
| 11 | Sample Applications - Presentations | Reading the lecture notes | |
| 12 | Sample Applications - Presentations | Reading the lecture notes | |
| 13 | Sample Applications - Presentations | Reading the lecture notes | |
| 14 | Sample Applications - Presentations | Reading the lecture notes | |
| 15 | Sample Applications - Presentations | Reading the lecture notes | |
| 16 | Term Exams | Term Exams | |
| 17 | Term Exams | Term Exams |
Assessment (Exam) Methods and Criteria
| Assessment Type | Midterm / Year Impact | End of Term / End of Year Impact |
|---|---|---|
| 1. Midterm Exam | 50 | 20 |
| 1. Project / Design | 50 | 20 |
| General Assessment | ||
| Midterm / Year Total | 100 | 40 |
| 1. Final Exam | - | 60 |
| Grand Total | - | 100 |
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 | ||