Course Information
SemesterCourse Unit CodeCourse Unit TitleT+P+LCreditNumber of ECTS CreditsLast Updated Date
1BBM413FUNDAMENTALS OF IMAGE PROCESSING3+0+03606.09.2024

 
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 The subject matter of this course is to provide an introduction to image processing
Course Content Image formation,
Point operations and histogram processing,
Spatial filtering techniques,
Frequency domain approaches,
Image smoothing,
Edge detection
Image segmentation
Deep learning basics
Convolutional neural networks
Course Methods and Techniques Lecture, Problem Solving
Prerequisites and co-requisities ( BBM104 ) and ( BBM415 ) and ( BBM102 )
Course Coordinator None
Name of Lecturers Associate Prof.Dr. Aydın Kaya
Assistants None
Work Placement(s) No

Recommended or Required Reading
Resources Computer Vision: Algorithms and Applications, Richard Szeliski, Springer, 2010 Digital Image Processing, R. C. Gonzalez, R. E. Woods, 3rd Edition, Prentice Hall, 2008
Course Notes Computer Vision: Algorithms and Applications, Richard Szeliski, Springer, 2010
Digital Image Processing, R. C. Gonzalez, R. E. Woods, 3rd Edition, Prentice Hall, 2008


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 % 30
Attendance 14 % 5
Project 1 % 25
Final examination 1 % 40
Total
17
% 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 14 3 42
Project 1 40 40
Preparation for Midterm Exam 1 24 24
General Exam Preparation 1 32 32
Total Work Load   Number of ECTS Credits 6 180

 
Course Learning Outcomes: Upon the successful completion of this course, students will be able to:
NoLearning Outcomes
1 After completing the course, the students will have an introduction to interrelated disciplines like image processing, computer vision and computational photography
2 gain deep understanding and knowledge of concepts that underlie image processing and related fields
3 read and discuss some research papers from the current literature.
4 Güncel literatürden makaleler ve yazılar okuyarak derinlemesine bilgi sahibi olur.
5  
6  
7  
8  

 
Weekly Detailed Course Contents
WeekTopicsStudy MaterialsMaterials
1 Introduction, image formation and the digital camera
2 Color perception, color spaces and point operations
3 Spatial filtering
4 Frequency domain techniques
5 Frequency domain techniques
6 Image pyramids and wavelets
7 Gradients, edges, contours
8 Image smoothing
9 Midterm exam
10 Image segmentation
11 Advanced Topics: Deep learning basics
12 Advanced Topics: Convolutional neural networks and applications
13 Project presentations
14 Project presentations
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 4 4 2 4 3 3 3 4 3 4 2
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=2687543&lang=en