DEPARTMENT OF SOFTWARE ENGINEERING (ENGLISH)
Bachelor	TR-NQF-HE: Level 6	QF-EHEA: First Cycle	EQF-LLL: Level 6

Ders Genel Tanıtım Bilgileri

Course Code:

1413002005

Ders İsmi:

Image Processing

Ders Yarıyılı:

Fall

Ders Kredileri:

Theoretical	Practical	Credit	ECTS
3	0	3	5

Language of instruction:

Ders Koşulu:

Ders İş Deneyimini Gerektiriyor mu?:

Type of course:

Department Elective

Course Level:

Bachelor

TR-NQF-HE:6. Master`s Degree

QF-EHEA:First Cycle

EQF-LLL:6. Master`s Degree

Mode of Delivery:

Face to face

Course Coordinator :

Dr.Öğr.Üyesi Adem ÖZYAVAŞ

Course Lecturer(s):

Course Assistants:

Dersin Amaç ve İçeriği

Course Objectives:

Within the scope of this course, it is aimed to teach advanced digital signal, image processing, pattern recognition and machine learning methods on biomedical data. The main aim of the course is to increase students' mathematical, scientific and computational analysis skills in this field. In this context, the content of the course includes the acquisition of biomedical data, evaluation of its properties, teaching the reasons and applications of the preprocessing steps (noise removal, filtering, reinforcement, size reduction, etc.), feature extraction, modeling, unsupervised and supervised learning, as well as semi-trained, community and Deep learning issues will also be discussed. In addition, Matlab and Python-based individual/group projects will be carried out on basic biomedical applications in order to increase the computational skills of the students.

Course Content:

Properties of biomedical signs and images; Transformation methods used in signal and image processing; Noise removal in signs and images; Signal and image filtering methods; Signal and image filtering methods; Linear and nonlinear dimension reduction methods; Statistical, morphological and spatial feature extraction methods; Instructed learning methods in signal and image processing; Unsupervised learning methods in signal and image processing; Semi-tutorial, community and deep learning methods.

Learning Outcomes

The students who have succeeded in this course;

Learning Outcomes

1 - Knowledge
Theoretical - Conceptual
1) Students will be given a theoretical background on the origin and nature of signs and images.
2 - Skills
Cognitive - Practical
1) Computer engineering students will be provided with strong mathematical and algorithmic knowledge, especially in this developing interdisciplinary field.
3 - Competences
Communication and Social Competence
Learning Competence
1) Students will try to increase their computational and scientific abilities in subjects such as pattern recognition and machine learning, as well as sign and image processing.
Field Specific Competence
Competence to Work Independently and Take Responsibility

Ders Akış Planı

Week	Subject	Related Preparation
1)	Obtaining and characteristics of biomedical signs and images	Robotics, mechatronics, and artificial intelligence, Newton C. Braga, Elsevier, 2002.
2)	Analysis of statistical characteristics of signals (Moments, power, information, correlation...)	Robotics, mechatronics, and artificial intelligence, Newton C. Braga, Elsevier, 2002.
3)	Digital signal processing fundamentals, sampling, quantization	Robotics, mechatronics, and artificial intelligence, Newton C. Braga, Elsevier, 2002.
4)	Frequency analysis, Conversion methods I: DFT, DCT, STFT	Robotics, mechatronics, and artificial intelligence, Newton C. Braga, Elsevier, 2002.
5)	Transformation methods II: Wavelet transform
6)	Image processing basics	Robotics, mechatronics, and artificial intelligence, Newton C. Braga, Elsevier, 2002.
7)	Noise removal in image processing	Robotics, mechatronics, and artificial intelligence, Newton C. Braga, Elsevier, 2002.
8)	Midterm	Robotics, mechatronics, and artificial intelligence, Newton C. Braga, Elsevier, 2002.
9)	Filtering and consolidation methods	Robotics, mechatronics, and artificial intelligence, Newton C. Braga, Elsevier, 2002.
10)	Analysis of signs and images with supervised learning methods	Robotics, mechatronics, and artificial intelligence, Newton C. Braga, Elsevier, 2002.
11)	Dimension reduction and linear/non-linear transformation methods	Robotics, mechatronics, and artificial intelligence, Newton C. Braga, Elsevier, 2002.
12)	Fundamentals of pattern recognition and machine learning for biomedical signs and images	Robotics, mechatronics, and artificial intelligence, Newton C. Braga, Elsevier, 2002.
13)	Analysis of signs and images with unsupervised learning methods	Robotics, mechatronics, and artificial intelligence, Newton C. Braga, Elsevier, 2002.
14)	Analysis of signs and images with supervised learning methods	Robotics, mechatronics, and artificial intelligence, Newton C. Braga, Elsevier, 2002.
15)	Analysis of signs and images with semi-tutorial, ensemble and deep learning methods	Robotics, mechatronics, and artificial intelligence, Newton C. Braga, Elsevier, 2002.
16)	Final	Robotics, mechatronics, and artificial intelligence, Newton C. Braga, Elsevier, 2002.

Sources

Course Notes / Textbooks:	Okutman Notları
References:	Lecture Notes

Ders - Program Öğrenme Kazanım İlişkisi

Ders Öğrenme Kazanımları	1	2	3
Program Outcomes
1) Competent knowledge of mathematics, science and technology, and computer engineering; ability to apply this knowledge to engineering solutions.
2) Skills to design and conduct experiments, collect data, analyze and interpret results.
3) Ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; ability to apply modern design methods for this purpose.
4) Ability to develop, select and use modern techniques and tools required for analysis and solution of complex problems encountered in engineering practice; ability to use information technologies effectively.
5) Ability to design and conduct experiments, collect data, analyze and interpret results to investigate complex engineering problems or discipline-specific research topics.
6) Ability to work effectively in intra-disciplinary and multi-disciplinary teams; ability to work individually.
7) Ability to communicate effectively in Turkish, both orally and in writing; Knowledge of at least one foreign language; the ability to write and understand written reports effectively, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8) Awareness of the necessity of lifelong learning; the ability to access information, to follow developments in science and technology, and to constantly renew oneself.
9) Acting in accordance with ethical principles, professional and ethical responsibility awareness; information about standards used in engineering applications.
10) Information about business life practices such as project management, risk management and change management; awareness of entrepreneurship, innovation; information about sustainable development.
11) Knowledge about the universal and social effects of engineering applications on health, environment and safety and the problems of the age reflected in the field of engineering; awareness of the legal consequences of engineering solutions.

Ders - Öğrenme Kazanımı İlişkisi

No Effect	1 Lowest	2 Low	3 Average	4 High	5 Highest

	Program Outcomes	Level of Contribution
1)	Competent knowledge of mathematics, science and technology, and computer engineering; ability to apply this knowledge to engineering solutions.
2)	Skills to design and conduct experiments, collect data, analyze and interpret results.
3)	Ability to design a complex system, process, device or product under realistic constraints and conditions to meet specific requirements; ability to apply modern design methods for this purpose.
4)	Ability to develop, select and use modern techniques and tools required for analysis and solution of complex problems encountered in engineering practice; ability to use information technologies effectively.
5)	Ability to design and conduct experiments, collect data, analyze and interpret results to investigate complex engineering problems or discipline-specific research topics.
6)	Ability to work effectively in intra-disciplinary and multi-disciplinary teams; ability to work individually.
7)	Ability to communicate effectively in Turkish, both orally and in writing; Knowledge of at least one foreign language; the ability to write and understand written reports effectively, to prepare design and production reports, to make effective presentations, to give and receive clear and understandable instructions.
8)	Awareness of the necessity of lifelong learning; the ability to access information, to follow developments in science and technology, and to constantly renew oneself.
9)	Acting in accordance with ethical principles, professional and ethical responsibility awareness; information about standards used in engineering applications.
10)	Information about business life practices such as project management, risk management and change management; awareness of entrepreneurship, innovation; information about sustainable development.
11)	Knowledge about the universal and social effects of engineering applications on health, environment and safety and the problems of the age reflected in the field of engineering; awareness of the legal consequences of engineering solutions.

Öğrenme Etkinliği ve Öğretme Yöntemleri

Course
Homework

Ölçme ve Değerlendirme Yöntemleri ve Kriterleri

Yazılı Sınav (Açık uçlu sorular, çoktan seçmeli, doğru yanlış, eşleştirme, boşluk doldurma, sıralama)
Homework

Assessment & Grading

Semester Requirements	Number of Activities	Level of Contribution
Attendance	10	% 10
Project	1	% 20
Midterms	1	% 30
Semester Final Exam	1	% 40
total		% 100
PERCENTAGE OF SEMESTER WORK		% 60
PERCENTAGE OF FINAL WORK		% 40
total		% 100

İş Yükü ve AKTS Kredisi Hesaplaması

Activities	Number of Activities	Duration (Hours)	Workload
Course Hours	14	3	42
Study Hours Out of Class	35	3	105
Midterms	1	2	2
Final	1	3	3
Total Workload			152