Course Information

Semester	Course Unit Code	Course Unit Title	T+P+L	Credit	Number of ECTS Credits	Last Updated Date
8	EEE 418	Computer Vision	3+0+0	3	5	24.08.2022

Course Details

Language of Instruction	English
Level of Course Unit	Bachelor's Degree
Department / Program	Electrical and Electronics Engineering
Type of Program	Formal Education
Type of Course Unit	Elective
Course Delivery Method	Face To Face
Objectives of the Course	Giving basic concepts of programming, gaining experience in producing solutions in Matlab programming for computer vision.
Course Content	Image formation, representation, improvements, transformations, elaborations, recognition / matching.
Course Methods and Techniques
Prerequisites and co-requisities	None
Course Coordinator	None
Name of Lecturers	Asist Prof.Dr. Yalçın Albayrak
Assistants	None
Work Placement(s)	No

Recommended or Required Reading

Resources

Computer vision, Linda Shapiro, George Stockman, Prentince Hall

Course Category

Mathematics and Basic Sciences	%10
Engineering	%20
Engineering Design	%30
Field	%40

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

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
Assignments	1	3	3
Mid-terms	1	2	2
Final examination	1	3	3
Total Work Load			Number of ECTS Credits 3 92

Course Learning Outcomes: Upon the successful completion of this course, students will be able to:

No	Learning Outcomes
1	To be able to explain different rendering methods and different image representations.
2	To be able to explain and apply image transformation, enhancement and analysis algorithms.
3	To be able to perform image transformation and analysis processes using Matlab / Octave programming tools.
4	Analyzing computer vision problems and developing solutions.

Weekly Detailed Course Contents

Week	Topics	Study Materials	Materials
1	Introduction to Computer Vision, image modalities
2	Image representation and introduction to Matlab/Octave
3	Binary Images and connected component labelling algorithms, morphological operations
4	Region properties, gray-scale, color images
5	Geometric and intensity transformations. Histogram and histogram equalization.
6	Spatial filtering: linear and non-linear filters.
7	Edge detection
8	Line detection, Hough transform.
9	Image projections, template matching, similarity, normalized cross correlation
10	Fourier basis, transform, and frequency domain filtering
11	Image pyramids, local interest points, features and matching
12	Visual recognition, object detection
13	Pattern recognition/classification
14	Review

Contribution of Learning Outcomes to Programme Outcomes

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