Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS Credits
8MKTS417Robot Vision3+0+036

Course Details
Language of Instruction Turkish
Level of Course Unit Bachelor's Degree
Department / Program Computer Engineering
Mode of Delivery Face to Face
Type of Course Unit Elective
Objectives of the Course 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 Methods and Techniques
Prerequisites and co-requisities None
Course Coordinator None
Name of Lecturers Asist Prof. Aytaç Uğur YERDEN
Assistants None
Work Placement(s) No

Recommended or Required Reading
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.
Lectures, Question-Answer, Project.
Bir grup projesi
Vize ve Final Sınavları

Course Category
Mathematics and Basic Sciences %0
Engineering %10
Engineering Design %10
Social Sciences %0
Education %0
Science %0
Health %0
Field %80

Planned Learning Activities and Teaching Methods
Activities are given in detail in the section of "Assessment Methods and Criteria" and "Workload Calculation"

Assessment Methods and Criteria
In-Term Studies Quantity Percentage
Mid-terms 1 % 40
Final examination 1 % 60
Total
2
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 3 42
Hours for off-the-c.r.stud 14 3 42
Mid-terms 1 2 2
Practice 14 2 28
Project 1 10 10
Final examination 1 2 2
Total Work Load   Number of ECTS Credits 4 126

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 Understand computer vision hardware and software elements
2 Understand computer vision systems
3 Constitute image processing algorithms and code them
4 Design an industrial image processing system.


Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
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 Review.
8 Midterm exam.
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 Review.


Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11
All 1 5 5 5 5 5 5 5 5 5
C1 1 5 5 5 5 5 5 5 5 5
C2 1 5 5 5 5 5 5 5 5 5
C3 1 5 5 5 5 5 5 5 5 5
C4 1 5 5 5 5 5 5 5 5 5

bbb


https://obs.gedik.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=206147&curProgID=5607&lang=en