CENG712 Intelligent Computational Imagıng and Video

6 ECTS - 3-0 Duration (T+A)- . Semester- 3 National Credit

Information

Code CENG712
Name Intelligent Computational Imagıng and Video
Term 2023-2024 Academic Year
Semester . Semester
Duration (T+A) 3-0 (T-A) (17 Week)
ECTS 6 ECTS
National Credit 3 National Credit
Teaching Language İngilizce
Level Doktora Dersi
Type Normal
Mode of study Yüz Yüze Öğretim
Catalog Information Coordinator Doç. Dr. MUSTAFA ORAL


Course Goal / Objective

To introduce students the fundamentals of image formation; To introduce students the major ideas, methods, and techniques of computer vision and pattern recognition; To develop an appreciation for various issues in the design of computer vision and object recognition systems; and To provide the student with programming experience from implementing computer vision and object recognition applications

Course Content

introduction to course;An overview of the intelligent systems;An overview of image processing;Feature and corner detection;Feature descriptors and matching;High Dynamic Range imaging;Camera models;Stereo;Structure from motion; techniques for combining multiple images;Tone Reproduction for Realistic Images; Intelligent imaging applications

Course Precondition

None

Resources

GONZALEZ R.C., WOODS R.E., and ADDINS S.L., Digital Image Processing Using Matlab, Pearson Education Inc., New Jersey, 2004.

Notes

AWCOCK G.J. and THOMAS R., Applied Image Processing, McGrow-Hill, Inc., 1996. 4.


Course Learning Outcomes

Order Course Learning Outcomes
LO01 identify basic concepts, terminology, theories, models and methods in the field of computer vision
LO02 identify basic concepts, terminology, theories, models and methods in the field of artificial intelligence
LO03 describe basic methods of computer vision related to multi-scale representation, edge detection and detection of other primitives, stereo, motion and object recognition
LO04 develop and apply computer vision techniques for solving practical problems


Relation with Program Learning Outcome

Order Type Program Learning Outcomes Level
PLO01 Bilgi - Kuramsal, Olgusal On the basis of the competencies gained at the undergraduate level, it has an advanced level of knowledge and understanding that provides the basis for original studies in the field of Computer Engineering. 4
PLO02 Bilgi - Kuramsal, Olgusal By reaching scientific knowledge in the field of engineering, he/she reaches the knowledge in depth and depth, evaluates, interprets and applies the information. 2
PLO03 Yetkinlikler - Öğrenme Yetkinliği Being aware of the new and developing practices of his / her profession and examining and learning when necessary. 3
PLO04 Yetkinlikler - Öğrenme Yetkinliği Constructs engineering problems, develops methods to solve them and applies innovative methods in solutions. 3
PLO05 Yetkinlikler - Öğrenme Yetkinliği Designs and applies analytical, modeling and experimental based researches, analyzes and interprets complex situations encountered in this process.
PLO06 Yetkinlikler - Öğrenme Yetkinliği Develops new and / or original ideas and methods, develops innovative solutions in system, part or process design. 4
PLO07 Beceriler - Bilişsel, Uygulamalı Has the skills of learning. 4
PLO08 Beceriler - Bilişsel, Uygulamalı Being aware of new and emerging applications of Computer Engineering examines and learns them if necessary. 3
PLO09 Beceriler - Bilişsel, Uygulamalı Transmits the processes and results of their studies in written or oral form in the national and international environments outside or outside the field of Computer Engineering. 1
PLO10 Beceriler - Bilişsel, Uygulamalı Has comprehensive knowledge about current techniques and methods and their limitations in Computer Engineering. 4
PLO11 Beceriler - Bilişsel, Uygulamalı Uses information and communication technologies at an advanced level interactively with computer software required by Computer Engineering. 3
PLO12 Bilgi - Kuramsal, Olgusal Observes social, scientific and ethical values in all professional activities.


Week Plan

Week Topic Preparation Methods
1 introduction to course Reading course material Öğretim Yöntemleri:
Anlatım, Soru-Cevap
2 An overview of the intelligent systems first part Reading course material Öğretim Yöntemleri:
Anlatım, Soru-Cevap
3 An overview of the intelligent systems 2nd part Reading course material Öğretim Yöntemleri:
Anlatım, Soru-Cevap
4 An overview of image processing first part Reading course material Öğretim Yöntemleri:
Anlatım, Soru-Cevap
5 An overview of image processing 2nd part Reading course material Öğretim Yöntemleri:
Anlatım, Soru-Cevap
6 Feature and corner detection Reading course material Öğretim Yöntemleri:
Anlatım, Soru-Cevap
7 Feature descriptors and matching Reading course material Öğretim Yöntemleri:
Anlatım, Soru-Cevap
8 Mid-Term Exam Exam preparation Ölçme Yöntemleri:
Yazılı Sınav
9 High Dynamic Range imaging Reading course material Öğretim Yöntemleri:
Anlatım
10 Camera models Reading course material Öğretim Yöntemleri:
Anlatım
11 Stereo Reading course material Öğretim Yöntemleri:
Anlatım, Soru-Cevap
12 Structure from motion Reading course material Öğretim Yöntemleri:
Soru-Cevap, Anlatım, Tartışma
13 techniques for combining multiple images (basic methods) Reading course material Öğretim Yöntemleri:
Anlatım
14 techniques for combining multiple images (advanced methods) Reading course material Öğretim Yöntemleri:
Anlatım
15 Intelligent imaging applications Reading course material Öğretim Yöntemleri:
Anlatım, Tartışma, Gösteri
16 Term Exams Project Development and presentation Ölçme Yöntemleri:
Proje / Tasarım, Performans Değerlendirmesi
17 Term Exams Project Development and presentation Ölçme Yöntemleri:
Proje / Tasarım, Performans Değerlendirmesi


Student Workload - ECTS

Works Number Time (Hour) Workload (Hour)
Course Related Works
Class Time (Exam weeks are excluded) 14 3 42
Out of Class Study (Preliminary Work, Practice) 14 5 70
Assesment Related Works
Homeworks, Projects, Others 0 0 0
Mid-term Exams (Written, Oral, etc.) 1 15 15
Final Exam 1 30 30
Total Workload (Hour) 157
Total Workload / 25 (h) 6,28
ECTS 6 ECTS

Update Time: 09.05.2023 07:28