Course Name Code Semester T+U Hours Credit ECTS
Computer Vision BSM 466 8 3 + 0 3 5
Precondition Courses
Recommended Optional Courses
Course Language Turkish
Course Level Bachelor's Degree
Course Type Optional
Course Coordinator Dr.Öğr.Üyesi SERAP ÇAKAR
Course Lecturers Dr.Öğr.Üyesi SERAP ÇAKAR,
Course Assistants
Course Category
Course Objective It is generally necesary to use a computer vision system in an industrial automation system. Especially, part counting, quality control and other applications like these are generally done by computer vision.

In this course, the aim is make students learn image processing methods, and develop a computer vision system for an industrial application.
Course Content Introduction to computer vision. To form an image matrix and neighbourhood operations. Hardware and software architecture of a computer vision system. Gray level, binary and color image processing methods. Quantizing, noise reduction. Edge detection. Feature extraction. Fundamentals of 3-D image processing. Sample applications
# Course Learning Outcomes Teaching Methods Assessment Methods
1 Understand computer vision hardware and software elements Lecture, Self Study, Testing, Homework, Project / Design,
2 Understand computer vision systems Lecture, Drilland Practice, Self Study, Project Based Learning, Testing, Homework, Project / Design,
3 Constitute image processing algorithms and code them Lecture, Drilland Practice, Problem Solving, Project Based Learning, Testing, Homework, Project / Design,
4 Design an industrial image processing system. Self Study, Problem Solving, Project Based Learning, Homework, Project / Design,
Week Course Topics Preliminary Preparation
1 Introduction to computer vision
2 Hardware and sofware architecture of a computer vision system
3 Forming an image matrix and neighbourhood operations.
4 Gray level, binary and color image processing and their usage area.
5 Quantizing, Threshold, histogram and noise reduction techinques.
6 Edge detection and corner detection
7 Image analysis towards pattern recognition
8 Pixel based operations on images.
9 Feature extraction for computer vision based classification applications.
10 Image processing in automatic visual inspection and quality control systems
11 Fundamentals of 3-D Image processing
12 Industrial computer vision applications and presentations by students.
13 Sample applications and presentations by students.
14 Sample applications and presentations by students.
Course Notes Lecture Notes
Course Resources 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.
5. DAVIES, E.R., Machine vision: Theory, Algorithms, Practicalities, Academic Pres, 1997.
6.. UMBAUGH S. E., Computer Vision and Image Processing, Prentice-Hall, 1998, USA.
Order Program Outcomes Level of Contribution
1 2 3 4 5
1 To have sufficient foundations on engineering subjects such as science and discrete mathematics, probability/statistics; an ability to use theoretical and applied knowledge of these subjects together for engineering solutions, X
2 An ability to determine, describe, formulate and solve engineering problems; for this purpose, an ability to select and apply proper analytic and modeling methods,al background in describing, formulating, modeling and analyzing the engineering problem, with a consideration for appropriate analytical solutions in all necessary situations X
3 An ability to select and use modern techniques and tools for engineering applications; an ability to use information technologies efficiently, X
4 An ability to analyze a system, a component or a process and design a system under real limits to meet desired needs; in this direction, an ability to apply modern design methods, X
5 An ability to design, conduct experiment, collect data, analyze and comment on the results and consciousness of becoming a volunteer on research,
6 Understanding, awareness of administration, control, development and security/reliability issues about information technologies,
7 An ability to work efficiently in multidisciplinary teams, self confidence to take responsibility, X
8 An ability to present himself/herself or a problem with oral/written techniques and have efficient communication skills; know at least one extra language, X
9 An awareness about importance of lifelong learning; an ability to update his/her knowledge continuously by means of following advances in science and technology, X
10 Understanding, practicing of professional and ethical responsibilities, an ability to disseminate this responsibility on society,
11 An understanding of project management, workplace applications, health issues of laborers, environment and job safety; an awareness about legal consequences of engineering applications, X
12 An understanding universal and local effects of engineering solutions; awareness of entrepreneurial and innovation and to have knowledge about contemporary problems.
Evaluation System
Semester Studies Contribution Rate
1. Ara Sınav 60
1. Kısa Sınav 10
2. Kısa Sınav 10
1. Performans Görevi (Seminer) 20
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
Quiz 2 4 8
Assignment 1 8 8
Final examination 1 15 15
Total Workload 137
Total Workload / 25 (Hours) 5.48
dersAKTSKredisi 5