Course Name Code Semester T+U Hours Credit ECTS
Digital Image Processing BSM 603 0 3 + 0 3 6
Precondition Courses
Recommended Optional Courses
Course Language Turkish
Course Level Doctorate Degree
Course Type Optional
Course Coordinator Doç.Dr. DEVRİM AKGÜN
Course Lecturers
Course Assistants
Course Category
Course Objective

The aim of this course is to teach image processing methods and to realize their algorithms with open source software.

Course Content

Introduction to image processing, gray level, binary and color image processing techniques. Digitization and quantization. Noise reduction algorithms. Edge detection algorithms and edge sharpening. Image segmentation. Thresholding  and automatic threshold value selection methods. Morphological and other regional operators. Image enhancement and repair. Histogram equalization. Image processing applications

# Course Learning Outcomes Teaching Methods Assessment Methods
Week Course Topics Preliminary Preparation
1 Introduction to image processing, OpenCV examples using Python
2 Threshold, automatic threshold value selection methods
3 Image filtering with two-dimensional convolution
4 Gaussian, Median and bilateral filters
5 Edge detection
6 Morphological transformations
7 Image Pyramids
8 Contours
9 Histogram operations
10 Image enhancement
11 Object recognition methods
12 Object recognition methods
13 Image segmentation
14 Project presentations
Resources
Course Notes
Course Resources

Gonzalez C. R., Woods E. R., Digital Image Processing, Pearson Education 2008 3rd ed.

McAndrew, Alasdair. A computational introduction to digital image processing. Chapman and Hall/CRC, 2015.

Wilhelm, Burger, and J. Burge Mark. "Principles of digital image processing: core algorithms." 2009

Solem, Jan Erik.Programming Computer Vision with Python: Tools and algorithms for analyzing images. " O'Reilly Media, Inc.", 2012.

Order Program Outcomes Level of Contribution
1 2 3 4 5
1 ability to access wide and deep information with scientific researches in the field of Engineering, evaluate, interpret and implement the knowledge gained in his/her field of study X
2 ability to complete and implement “limited or incomplete data” by using the scientific methods. X
3 ability to consolidate engineering problems, develop proper method(s) to solve and apply the innovative solutions to them X
4 ability to develop new and original ideas and method(s), to develop new innovative solutions at design of system, component or process X
5 gain comprehensive information on modern techniques, methods and their borders which are being applied to engineering X
6 ability to design and apply analytical, modelling and experimental based research, analyze and interpret the faced complex issues during the design and apply process X
7 gain high level ability to define the required information and data X
8 ability to work in multi-disciplinary teams and to take responsibility to define approaches for complex situations X
9 systematic and clear verbal or written transfer of the process and results of studies at national and international environments X
10 aware of social, scientific and ethical values guarding adequacy at all professional activities and at the stage of data collection, interpretation and announcement X
11 aware of new and developing application of profession and ability to analyze and study on those applications X
12 ability to interpret engineering application’s social and environmental dimensions and it’s compliance with the social environment X
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 50
1. Proje / Tasarım 30
1. Kısa Sınav 10
1. Ödev 10
Total 100
1. Yıl İçinin Başarıya 50
1. Final 50
Total 100
ECTS - Workload Activity Quantity Time (Hours) Total Workload (Hours)
Course Duration (Including the exam week: 16x Total course hours) 16 3 48
Hours for off-the-classroom study (Pre-study, practice) 16 3 48
Mid-terms 1 10 10
Assignment 1 5 5
Final examination 1 20 20
Quiz 1 5 5
Project / Design 1 15 15
Total Workload 151
Total Workload / 25 (Hours) 6.04
dersAKTSKredisi 6