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About the Course

Department Profile
Course unit title Level of course unit Course unit code Type of course unit Semester of course unit Local credit ECTS credit Syllabus
ADVANCED IMAGE PROCESSING TECHNIQUES AND COMPUTER Third cycle BİM 522 2 7.50 7.50 Print
   
Description of course unit
Prerequisites and course requisities no
Language of instruction TURKISH
Coordinator Assoc. Prof. Veysel Aslantas
Lecturer(s) Assoc. Prof. Veysel Aslantas
Teaching assitant(s) NO
Mode of delivery Classroom lectures
Course objective pon successful completion of this course of study a student: • Knows imaging models and how images are generated. • Understands how to model linear systems and knows how to implement linear and nonlinear filtering. • Knows how to design and implement edge detection algorithms. • Understands the basics of texture modeling and algorithms for texture classification. • Knows the basic principles of motion estimation and how to implement optical flow estimation algorithms. • Understands the fundamental problems and importance of segmentation and grouping and knows how to implement basic segmentation and grouping algorithms. • Understands the issues in object recognition from images and knows how to implement basic template matching and deformable template matching algorithms. • Has some experience with research in computer vision.
Course description Robotics and industrial control systems are becoming one of the fastest growing fields with computer science. More and more companies are automating with computer controlled robotic machinery. Automated manufacturing is leading the advanced technology revolution as companies vie for the competitive edge utilizing the productive, efficient, computer controlled robots. This course is open to graduate students. The course aims at providing advanced techniques of Image Processing and Computer Vision. The techniques for image processing and feature extraction are covered in lectures; topics include: Geometric transforms, Discrete transforms, Noise, filter design, noise removing, Image restoration, Edge detection techniques, Object features extraction and analysis, Image analysis, Region, contour and motion based image segmentation techniques.

Course contents
1 Geometric transforms
2 Discrete transforms
3 Discrete transforms
4 Noise, filter design, noise removing
5 Image restoration
6 Image restoration
7 Edge detection techniques
8 MID-TERM EXAM
9 Object features extraction and analysis
10 Object features extraction and analysis
11 Image analysis
12 Image analysis
13 contour and motion based image segmentation techniques
14 contour and motion based image segmentation techniques
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Learning outcomes of the course unit
1 Apply math, science and engineering knowledge.
2 Design a system, component or process to meet desired needs.
3 Identify, formulate, and solve engineering problems.
4 learn image processing techniques
5 learn image processing concepts
6 develop image processing techniques
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*Contribution level of the course unit to the key learning outcomes
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Number of stars refer to level of contribution from 1 (the least) to 5 (the most)

Planned learning activities, teaching methods and ECTS work load
  Quantity Time (hour) Quantity*Time (hour)
Lectures (face to face teaching) 14 3 42
Study hours out of classroom (study before and after the class) 14 3 42
Homework 3 10 30
Presentation / seminar 1 10 10
Quiz 0 0 0
Preparation for midterm exams 1 20 20
Midterm exams 1 2 2
Project (term paper) 1 5 5
Laboratuar 0 0 0
Field study 1 30 30
Preparation for final exam 0 0 0
Final exam 1 2 2
Research 0 0 0
Total work load     183
ECTS     7.50

Assessment methods and criteria
Evaluation during semester Quantity Percentage
Midterm exam 1 40
Quiz 0 0
Homework 0 0
Semester total   40
Contribution ratio of evaluation during semester to success   0
Contribution ratio of final exam to success   60
General total   60

Recommended and required reading
Textbook • R. C. Gonzalez and R. E. Woods, Digital Image Processing, Addison-Wesley Pub. Co., New York, (2nd edition) 2002.
Additional references • Sonka, M., Hlavac, V., and Boyle, R. Image Processing, Analysis and Machine Vision. Chapman & Hall Computing, 1993. • Anil K. Jain, Fundamentals of Digital Image Processing, Prentice Hall, 1989. • Low, A. Introductory Computer Vision and Image Processing. McGraw-hill, 1991

Files related to the course unit