Understand and apply basic image processing techniques
12, 14, 6, 9
A, E
Understand and apply image formation and modeling concepts
12, 14, 16, 6, 9
A, E
Understand and apply mid-level computer vision techniques, including feature extraction and optical flow
12, 14, 16, 6, 9
A, E
Design and evaluate solutions to computer vision problems
12, 14, 16, 6, 9
A, E
Teaching Methods:
12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 6: Experiential Learning, 9: Lecture Method
Assessment Methods:
A: Traditional Written Exam, E: Homework
Course Outline
Order
Subjects
Preliminary Work
1
Optical image formation
2
Imaging pipeline
3
Image filtering
4
Edge detection and Hough transform
5
Morphological operations
6
Image enhancement
7
Keypoint detection (basic ideas)
8
Keypoint detection (scale invariant methods)
9
Image interpolation
10
Geometric transformations
11
Motion estimation
12
Camera calibration
13
3D vision
14
Color space
Resources
Sonka, Hlavac, and Boyle. “Image Processing, Analysis, and Machine Vision.” Cengage Learning, 4th edition.
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
An ability to apply knowledge of mathematics, science, and engineering
X
2
An ability to identify, formulate, and solve engineering problems
X
3
An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
X
4
An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice
X
5
An ability to design and conduct experiments, as well as to analyze and interpret data
X
6
An ability to function on multidisciplinary teams
X
7
An ability to communicate effectively
X
8
A recognition of the need for, and an ability to engage in life-long learning
9
An understanding of professional and ethical responsibility
10
A knowledge of contemporary issues
11
The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context
Assessment Methods
Contribution Level
Absolute Evaluation
Rate of Midterm Exam to Success
30
Rate of Final Exam to Success
70
Total
100
ECTS / Workload Table
Activities
Number of
Duration(Hour)
Total Workload(Hour)
Course Hours
14
3
42
Guided Problem Solving
0
0
0
Resolution of Homework Problems and Submission as a Report
1
30
30
Term Project
0
0
0
Presentation of Project / Seminar
0
0
0
Quiz
0
0
0
Midterm Exam
1
32
32
General Exam
1
32
32
Performance Task, Maintenance Plan
0
0
0
Total Workload(Hour)
136
Dersin AKTS Kredisi = Toplam İş Yükü (Saat)/30*=(136/30)
5
ECTS of the course: 30 hours of work is counted as 1 ECTS credit.
Detail Informations of the Course
Course Description
Course
Code
Semester
T+P (Hour)
Credit
ECTS
INTRODUCTION to COMPUTER VISION
-
Fall Semester
3+0
3
6
Course Program
Prerequisites Courses
Recommended Elective Courses
Language of Course
English
Course Level
First Cycle (Bachelor's Degree)
Course Type
Elective
Course Coordinator
Prof.Dr. Bahadır Kürşat GÜNTÜRK
Name of Lecturer(s)
Prof.Dr. Bahadır Kürşat GÜNTÜRK
Assistant(s)
Aim
To understand the basic topics in computer vision and to apply and evaluate various computer vision techniques.
Understand and apply basic image processing techniques
12, 14, 6, 9
A, E
Understand and apply image formation and modeling concepts
12, 14, 16, 6, 9
A, E
Understand and apply mid-level computer vision techniques, including feature extraction and optical flow
12, 14, 16, 6, 9
A, E
Design and evaluate solutions to computer vision problems
12, 14, 16, 6, 9
A, E
Teaching Methods:
12: Problem Solving Method, 14: Self Study Method, 16: Question - Answer Technique, 6: Experiential Learning, 9: Lecture Method
Assessment Methods:
A: Traditional Written Exam, E: Homework
Course Outline
Order
Subjects
Preliminary Work
1
Optical image formation
2
Imaging pipeline
3
Image filtering
4
Edge detection and Hough transform
5
Morphological operations
6
Image enhancement
7
Keypoint detection (basic ideas)
8
Keypoint detection (scale invariant methods)
9
Image interpolation
10
Geometric transformations
11
Motion estimation
12
Camera calibration
13
3D vision
14
Color space
Resources
Sonka, Hlavac, and Boyle. “Image Processing, Analysis, and Machine Vision.” Cengage Learning, 4th edition.
Course Contribution to Program Qualifications
Course Contribution to Program Qualifications
No
Program Qualification
Contribution Level
1
2
3
4
5
1
An ability to apply knowledge of mathematics, science, and engineering
X
2
An ability to identify, formulate, and solve engineering problems
X
3
An ability to design a system, component, or process to meet desired needs within realistic constraints such as economic, environmental, social, political, ethical, health and safety, manufacturability, and sustainability
X
4
An ability to use the techniques, skills, and modern engineering tools necessary for engineering practice
X
5
An ability to design and conduct experiments, as well as to analyze and interpret data
X
6
An ability to function on multidisciplinary teams
X
7
An ability to communicate effectively
X
8
A recognition of the need for, and an ability to engage in life-long learning
9
An understanding of professional and ethical responsibility
10
A knowledge of contemporary issues
11
The broad education necessary to understand the impact of engineering solutions in a global, economic, environmental, and societal context
