Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
8EEE 418Computer Vision3+0+03524.08.2022

 
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
In-Term Studies Quantity Percentage
Mid-terms 1 % 30
Assignment 1 % 30
Final examination 1 % 60
Total
3
% 120

 
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:
NoLearning 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
WeekTopicsStudy MaterialsMaterials
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
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11
All 3 3 3 3 3 2 2 2 2 2 2
C1 3 3 3 3 3 2 2 2 2 2 2
C2 3 3 3 3 3 2 2 2 2 2 2
C3 4 4 4 4 4 2 2 2 2 2 2
C4 5 5 5 5 5 2 2 2 2 2 2

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  https://obs.akdeniz.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=2429261&lang=en