Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1AIN431Introduction to Computer Vision3+0+03612.08.2025

 
Course Details
Language of Instruction English
Level of Course Unit Bachelor's Degree
Department / Program ARTIFICIAL INTELLIGENCE ENGINEERING
Type of Program Formal Education
Type of Course Unit Elective
Course Delivery Method Face To Face
Objectives of the Course Being an introduction to the common terminology and basic applications of computer vision, the aim of this course is to let students gain fundamental computer vision techniques and apply them to basic problems.
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
Prerequisites and co-requisities ( BBM102 ) and ( BBM104 ) and ( AIN433 )
Course Coordinator None
Name of Lecturers Associate Prof. Ali Seydi Keçeli
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 1 % 24
Practice 4 % 8
Project 1 % 28
Final examination 1 % 40
Total
7
% 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 10 2 20
Practice 4 1 4
Project 1 70 70
Preparation for Midterm Exam 1 15 15
General Exam Preparation 1 25 25
Total Work Load   Number of ECTS Credits 5,86666666666667 176

 
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 1- Have a general knowledge about computer vision techniques and problems.
2 2- Provide basic solutions to various computer vision problems.
3 3- Have the necessary background to start conducting research in the area.
4 4- Have knowledge about the potential application areas of computer vision.
5 5- Have project experience on computer vision.
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction
2 Filters, convolution
3 Texture analysis and synthesis, binary image analysis
4 Multiple view geometry and camera calibration
5 Invariant image features
6 Basic matching techniques
7 Midterm Exam
8 Convolutional Neural Networks
9 Convolutional Neural Networks
10 Object Recognition
11 Object Recognition
12 Video analysis
13 Optical flow
14 Recurrent Neural Networks and video applications
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 5 3 4 4 4 4 4 2 3 2 5
C1
C2
C3
C4
C5
C6
C7
C8

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

  
  https://bilsis.hacettepe.edu.tr/oibs/bologna/progCourseDetails.aspx?curCourse=2733663&lang=en