SECTION I: GENERAL INFORMATION ABOUT THE COURSE

Course Code	Course Name	Year	Semester	Theoretical	Practical	Credit	ECTS
70610MEEOS-CME0690	Image Processing	1	Spring	3	0	3	6

Course Type :	Departmental Elective
Cycle:	Master TQF-HE:7. Master`s Degree QF-EHEA:Second Cycle EQF-LLL:7. Master`s Degree
Language of Instruction:	English
Prerequisities and Co-requisities:	N/A
Mode of Delivery:	Face to face
Name of Coordinator:	Profesör Dr. ABDURAZZAG ALI A ABURAS
Dersin Öğretim Eleman(lar)ı:
Dersin Kategorisi:	Programme Specific

SECTION II: INTRODUCTION TO THE COURSE

Course Objectives & Content

Course Objectives:

Course Learning Outcomes (CLOs) are those describing the knowledge, skills, and competencies that students are expected to achieve upon successful completion of the course. In this context, the course Learning Outcomes defined for this course unit are as follows:
1) Knowledge (Described as Theoretical and/or Factual Knowledge)
2) Skills (Describe as Cognitive and/or Practical Skills.)
3) Competences (Described as the "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field-specific" competencies.)

Course Content:

1) Introduction to Computer Vision.
2) Color Texture Image Basics
3) Image Coordinates and Resizing
4) Digital Filters, and Image Transformations
5) Edges and Features
6) Corner Detection-Edges and Features
7) Describing and Matching
8) Matching and Blending
9) Content-Based Image Retrieval (CBIR) and the EM Algorithm
10) Features and Flow
11) Basics for Convolutional Neural Networks (CNN)
12) Generative Adversarial Networks (GANs)

Course Specific Rules

Linear algebra, basic calculus, and probability
Experience with image processing will help but is not necessary.
Experience with Python or Python-like languages will help

Course Learning Outcomes (CLOs)

Course Learning Outcomes (CLOs) are those describing the knowledge, skills and competencies that students are expected to achieve upon successful completion of the course. In this context, Course Learning Outcomes defined for this course unit are as follows:



Knowledge *(Described as Theoritical and/or Factual Knowledge.)*
Skills *(Describe as Cognitive and/or Practical Skills.)*
1) Ability to solve a complex problem using advanced image processing techniques.
Competences *(Described as "Ability of the learner to apply knowledge and skills autonomously with responsibility", "Learning to learn"," Communication and social" and "Field specific" competences.)*

Weekly Course Schedule

Week	Subject	Materials Sharing *
		Related Preparation	Further Study
1)	What is Computer Vision? image representation using math. image matrix repreparation. Image transformations Advanced Application of Image Processing Why is computer vision so hard? Fun with Color Image Resizing and Filtering Panorama Stitching Neural Networks PyTorch Deep Learning/Machine Learning Advanced Application of Image Processing	prepare some demos, program code of examples. Materyal	n/a
2)	What is Computer Vision? image representation using math. image matrix repreparation. Image transformations Advanced Application of Image Processing Why is computer vision so hard? Fun with Color Image Resizing and Filtering Panorama Stitching Neural Networks PyTorch Deep Learning/Machine Learning Advanced Application of Image Processing	prepare some demos, and program code examples. Materyal	n/a
3)	map of color The 2D array of color Grayscale images Geometric HSV to RGB More Details on Color Spaces Color histograms can represent an image. Local Binary Pattern (LBP) Measure	prepare some demos, and program code examples. Materyal	n/a
4)	An image is a matrix of light. Addressing pixels Color representation. Image interpolation and resizing A note on coordinates in images Nearest-Neighbor Interpolation Bilinear Interpolation Image resizing	prepare some demos, and program code examples. Materyal	n/a
5)	Convolution operation Box filters smooth image. Gaussians smoothing with Gaussians Highpass Kernel Identity Kernel Sharpen Kernel Emboss Kernel Sobel Kernels	prepare some demos, and program code examples. Materyal	n/a
6)	Cross-Correlation vs Convolution What’s an edge? Finding edges Image derivatives Laplacian (2nd derivative) LoG filter Difference of Gaussian (DoG) Gradient magnitude Canny Edge Detection Non-maximum suppression Hough Transform for lines and circles.	prepare some demos, and program code examples. Materyal	n/a
7)	Corner Detection Harris Matrix Estimating Response Harris Corner Detector Properties of the Harris corner detector The SIFT Key point localization	prepare some demos, and program code examples. Materyal	n/a
8)	Descriptors Simple Normalized Descriptor Orientation Normalization SIFT descriptor. Properties of SIFT Matching with Features Compute Transformations Image reprojection Image Warping Solving for homographies Direct Linear Transforms (n points) RANSAC for estimating the homography	prepare some demos, and program code examples. Materyal	n/a
9)	Filtering in the Frequency Domain (2D continuous and discrete Fourier transform and its inverse, relationship between spatial and frequency intervals.)
9)	Panorama algorithm Stitching panoramas Very bad for big panoramas (Triangle) How do we fix it? Cylinders build a panorama from two (or more) images. RANSAC for Homography Image Blending Feathering Effect of window (ramp-width) size Pyramid blending. Alpha Blending Gain Compensation: Getting rid of artifacts. Blending Comparison Recognizing Panoramas Finding the panoramas Creating Panoramas	prepare some demos, and program code examples. Materyal	n/a
10)	Panorama algorithm Stitching panoramas Very bad for big panoramas (Triangle) How do we fix it? Cylinders build a panorama from two (or more) images. RANSAC for Homography Image Blending Feathering Effect of window (ramp-width) size Pyramid blending. Alpha Blending Gain Compensation: Getting rid of artifacts. Blending Comparison Recognizing Panoramas Finding the panoramas Creating Panoramas	prepare some demos, and program code examples. Materyal	n/a
10)	Filtering in the Frequency Domain (2D discrete convolution theorem, frequency domain filtering fundamentals.)
11)	Filtering in the Frequency Domain (Low-pass, high-pass, band-reject and band-pass filters in the frequency domain.)
11)	Queries Commercial Systems Retrieval Features Indexing in the FIDS System Lead-in to Object Recognition Weakness of the EM Classifier Approach	prepare some demos, and program code examples. Materyal	n/a
12)	Image Restoration and Reconstruction (Noise models and estimating noise parameters, mean, order statistic and adaptive filters.)
12)	CNN Convolution Operation Learning Pooling CNN Structures Image Classification Semantic Segmentation Convolutional Neural Networks In PyTorch	prepare some demos, and program code examples. Materyal	n/a
13)	Unsupervised Learning: Autoencoders Unsupervised Learning: Variational Autoencoders Distributions during training GAN: Sample Architecture (DC-GAN) Bidirectional GAN (BiGAN) Conditional GAN (cGAN) Progressive Growing of GANs	prepare some demos, and program code examples. Materyal	n/a
14)	Course revision all the chapters	Course revision	n/a

*These fields provides students with course materials for their pre- and further study before and after the course delivered.

Level of Contribution of the Course to PLOs

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

	Programme Learning Outcomes	Contribution Level (from 1 to 5)
1)	Owns advanced theoretical and applied knowledge in the field of computer science and engineering.
2)	Owns the comprehensive knowledge about advanced techniques and methods and their limitations applied in the field of computer science and engineering.
3)	Reaches knowledge broadly and deeply by application and development in the field of computer science and engineering, evaluates, interprets and applies knowledge.
4)	Complements and applies knowledge with scientific methods using uncertain, limited or incomplete data; can use information from different disciplines together.
5)	Defines the problem, accesses data, uses knowledge from different disciplines, designs researches, designs system and process, develops solution methods in order to solve current problems in the field of computer science and engineering.
6)	Can work effectively in disciplinary and multi-disciplinary teams, lead such teams and develop solution approaches in complex situations; can work independently and take responsibility.
7)	Has awareness of the new and developing applications of his/her profession, examines and learns them when needed.
8)	Has the necessary skills and competencies to perform his/her profession in the most effective way and to constantly improve himself/herself.
9)	Acquires communication in a Foreign Language (English) competence defined on the level of at least B2 in European Language Portfolio.
10)	Observes social, scientific and ethical values in the stages of data collection, interpretation, announcement and in all professional activities.
11)	Knows the social, environmental, health, safety, legal aspects of engineering applications, project management and business life applications, and is aware of the constraints they impose on engineering applications.

SECTION IV: TEACHING-LEARNING & ASSESMENT-EVALUATION METHODS OF THE COURSE

Teaching & Learning Methods of the Course

(All teaching and learning methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Teaching and Learning Methods defined at the Programme Level	Teaching and Learning Methods Defined for the Course

Assessment & Evaluation Methods of the Course

(All assessment and evaluation methods used at the university are managed systematically. Upon proposals of the programme units, they are assessed by the relevant academic boards and, if found appropriate, they are included among the university list. Programmes, then, choose the appropriate methods in line with their programme design from this list. Likewise, appropriate methods to be used for the course units can be chosen among those defined for the programme.)

Aassessment and evaluation Methods defined at the Programme Level	Assessment and Evaluation Methods defined for the Course

Contribution of Assesment & Evalution Activities to Final Grade of the Course

Measurement and Evaluation Methods	# of practice per semester	Level of Contribution
Project	3	% 20.00
Midterms	1	% 30.00
Semester Final Exam	1	% 50.00
Total		% 100
PERCENTAGE OF SEMESTER WORK		% 50
PERCENTAGE OF FINAL WORK		% 50
Total		% 100

SECTION V: WORKLOAD & ECTS CREDITS ALLOCATED FOR THE COURSE

WORKLOAD OF TEACHING & LEARNING ACTIVITIES
Teaching & Learning Activities	# of Activities per semester	Duration (hour)	Total Workload
Course	14	3	42
Laboratory	0	0	0
Application	0	0	0
Special Course Internship (Work Placement)	0	0	0
Field Work	0	0	0
Study Hours Out of Class	0	0	0
Presentations / Seminar	0	0	0
Project	3	0	0
Homework Assignments	0	0	0
Total Workload of Teaching & Learning Activities	-	-	42
WORKLOAD OF ASSESMENT & EVALUATION ACTIVITIES
Assesment & Evaluation Activities	# of Activities per semester	Duration (hour)	Total Workload
Quizzes	0	0	0
Midterms	1	3	3
Semester Final Exam	1	3	3
Total Workload of Assesment & Evaluation Activities	-	-	6
TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities)			48
ECTS CREDITS OF THE COURSE (Total Workload/25.5 h)			6

Course Notes / Textbooks:	all are available on OIS
References:	1) Recommended: Computer Vision: Algorithms and Applications Richard Szelisk, 2010 2) Hands-On Image Processing with Python: Expert techniques for advanced image analysis and effective interpretation of image data by Sandipan Dey 2025, ISBN: 978-1789343731