Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1BBM418COMPUTER VISION LABORATORY0+2+01406.09.2024

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program COMPUTER ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course To help students gain pratical skills on fundamental applications of computer vision.
Course Content Physics of image formation, image representation, geometrical transformations, binary image analysis, point and blob processing, filters, convolution, edge detection, texture analysis and synthesis, color spaces and models, invariant image features, optical flow, basic matching techniques.
Course Methods and Techniques Lecture, Drill and Practice, Problem Solving, Preparing and/or Presenting Reports
Prerequisites and co-requisities ( BBM104 ) and ( BBM416 ) and ( BBM102 )
Course Coordinator None
Name of Lecturers Associate Prof.Dr. Nazlı İkizler Cinbiş
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources David Forsyth, Jean Ponce "Computer Vision: A Modern Approach" 2nd edition, 2012, Prentice Hall. Richard Szeliski, "Computer Vision: Algorithms and Applications", Springer, 2011.
Course Notes David Forsyth, Jean Ponce "Computer Vision: A Modern Approach" 2nd edition, 2012, Prentice Hall
Richard Szeliski, “Computer Vision: Algorithms and Applications”, Springer, 2011


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
Midterm Exam 4 % 20
Assignment 2 % 50
Final examination 1 % 30
Total
7
% 100

 
ECTS Allocated Based on Student Workload
Activities Quantity Duration Total Work Load
Course Duration 14 2 28
Hours for off-the-c.r.stud 7 1 7
Assignments 2 25 50
Preparation for Midterm Exam 4 2,5 10
General Exam Preparation 1 25 25
Total Work Load   Number of ECTS Credits 4 120

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 In completition of this course, the student will gain practical knowledge on developing computer vision applications.
2 Improve the problem analysis and solving skills on computer vision.
3 Be able to conduct initial research and produce reports on the subject.
4 Implement computer vision algorithms in various platforms.
5 4- Çeşitli bilgisayarlı görü platformlarında uygulama yapar.
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction
2 Filters and Convolution
3 Binary image analysis
4 Texture analysis and synthesis
5 Invariant image features
6 Basic matching techniques
7 Midterm Exam
8 Convolutional Neural Networks
9 Convolutional Neural Networks
10 Object recognition
11 Object Detection
12 Video analysis
13 Optical flow
14 Recurrent Neural Networks
15 Final exam preparation
16 Final exam

 
Contribution of Learning Outcomes to Programme Outcomes
P1 P2 P3 P4 P5 P6 P7 P8 P9 P10 P11 P12
All 5 4 3 4 5 3 3 4 2 2 2
C1
C2
C3
C4
C5
C6
C7
C8

  Contribution: 1: Very Slight 2:Slight 3:Moderate 4:Significant 5:Very Significant

  
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