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    SECTION I: GENERAL INFORMATION ABOUT THE COURSE
    Course Code Course Name Year Semester Theoretical Practical Credit ECTS
    60610MEEOS-CME0437 Image Processing 4 Spring 2 2 3 7
    Course Type : Elective Course IV
    Cycle: Bachelor      TQF-HE:6. Master`s Degree      QF-EHEA:First Cycle      EQF-LLL:6. 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: 1. To familiarize students with digital image fundamentals and image transformations.

    2. To Introduce algorithms for spatial and frequency domain filtering.

    3. To teach students featured image processing applications with deep learning practices.
    Course Content: D1) Digital image fundamentals
    (Image formation model, sampling, and quantization, representing digital images, spatial and intensity resolution, and image interpolations.)
    2) Digital image fundamentals
    (Basic vector and matrix operations, single pixel and neighborhood operations, geometric transformations, image transforms)
    3) Intensity Transformations
    (Fundamentals of intensity transformation, Image negatives, log and gamma transformations, contrast stretching, intensity level, and bit-plane Slicing.)
    4) Intensity Transformations
    (Histogram processing, histogram equalization, histogram matching, local histogram processing, histogram statistics.)
    5) Spatial filtering
    (Fundamentals of spatial filtering, spatial correlation and convolution, separable filter kernels).
    6) Spatial filtering
    (Smoothing (low-pass) box and Gaussian kernels, sharpening (high-pass) filter kernels, image sharpening using first-second order derivatives.)
    7) Filtering in the Frequency Domain
    (Complex numbers, Fourier series, Fourier transform of 1D continuous functions, sampling theorem, and Fourier transform of 1D sampled functions.)
    8) Filtering in the Frequency Domain
    (2D continuous and discrete Fourier transform and its inverse, relationship between spatial and frequency intervals.)
    9) Filtering in the Frequency Domain
    (2D discrete convolution theorem, frequency domain filtering fundamentals.)
    10) Filtering in the Frequency Domain
    (Low-pass, high-pass, band-reject, and band-pass filters in the frequency domain.)
    11) Image Restoration and Reconstruction
    (Noise models and estimating noise parameters, mean, order statistic, and adaptive filters.)

    Course Specific Rules

    Course Prerequisites:
    Programming skills (C/C++, VC++, Python, MATLAB)
    Good math background (Calculus, Linear Algebra, Statistical Methods)

    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) The student will increase his/her knowledge and learning based each lecture related to the sequence of the given chapters during the semester weekly schedule.
      2) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.
      3) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.
      4) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.
      5) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.
      6) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule
      7) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.
      8) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.
      9) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.
      10) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.
      11) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.
      12) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.
      13) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.
      14) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.
      15) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.
      16) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.
      17) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.
      18) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.
      19) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.
      20) The student will increase his/her knowledge and learning based on each lecture related to the sequence of the given chapters during the semester's weekly schedule.
    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) Introduction review the chapter. Materyal N/A
    2) Basic vector and matrix operations, single pixel and neighbourhood operations, geometric transformations, and image transforms. review the chapter Materyal n/a
    3) Common transformations of the digital image Scaling 2-D Rotation (around the origin) Basic 2D transformations 2D Affine Transformations Projective Transformations solving for translation Iterative Closest Points (ICP) Algorithm Object Instance Recognition Keypoint Matching Algorithm review the chapter. Materyal n/a
    4) Common transformations of the digital image Scaling 2-D Rotation (around the origin) Basic 2D transformations 2D Affine Transformations Projective Transformations solving for translation Iterative Closest Points (ICP) Algorithm Object Instance Recognition Keypoint Matching Algorithm continue from the week. 3 Materyal n/a
    5) Image projection by math's representation. Goals of Image Analysis Digital Image Terminology The Three Stages of Computer Vision/Image Processing Projection: world coordinates  image coordinates Homogeneous coordinates Projection matrix Inserting photographed objects/Text into images Oriented and Translated Camera Orthographic Projection Field of View (Zoom, focal length) prepare some program codes. Materyal n/a
    6) continue from the Week. 5 as in week 5 Materyal n/a
    7) Filtering Hybrid Images Two views/types of filtering box filter. Image filtering operation Practice with linear filters. Sharpening Sobel filter Another filter: blur Better smoothing with Gaussians what was that convolution? Highpass Kernel/filter. Identity Kernel/filter. Sharpen Kernel/filter. Emboss Kernel/filter. Highpass Kernel/filter. 2d convolution Key properties of linear filters Important filter: Gaussian Smoothing with Gaussian filter Separability of the Gaussian filter the filter windows. prepare some program code examples. Materyal n/a
    8) as in the week 7 prepare some program code examples. Materyal n/a
    9) Differentiation and convolution Finite difference filters (Prewitt, Roberts) Effects of noise Derivative theorem of convolution Derivative of Gaussian filter Designing an edge detector Canny edge detector thinning Hysteresis thresholding Effect of  (Gaussian kernel spread/size) Median filter prepare some program code examples. Materyal n/a
    10) Frequency Spectra Fourier analysis in images Fourier Transform equations. Fourier Bases Computing the Fourier Transform The Convolution Theorem Properties of Fourier Transforms Filtering in the frequency domain Is convolution invertible? prepare some program code examples. Materyal n/a
    11) A Note About Grey Levels What Is Image Enhancement? Spatial & Frequency Domains Image Histograms Normalized histogram Image Contrast Histograms and Contrast prepare some program code examples. Materyal n/a
    12) Noise reduction Cross-correlation Mean filtering Sharpening revisited Filters: Thresholding prepare some program code examples. Materyal n/a
    13) Sharpening Spatial Filters Sharpening edge by First and second-order derivatives Discrete form of Laplacian Unsharp masking Gradient mask prepare some program code examples. Materyal n/a
    14) all chapters course revision n/a
    *These fields provides students with course materials for their pre- and further study before and after the course delivered.

    Recommended or Required Reading & Other Learning Resources/Tools

    Course Notes / Textbooks: Lecture material will be made available on OnlineBeykoz
    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

    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) Has sufficient knowledge in mathematics, science, computer science and computer engineering; use theoretical and applied knowledge in these fields together to solve computer engineering problems
    2) Uses and applies theoretical and applied sciences in the field of basic science subjects for the solution of computer engineering problems.
    3) Analyzes computer engineering applications, designs and develops models to meet specific requirements under realistic constraints and conditions. For this purpose, selects and uses appropriate methods, tools and technologies.
    4) Identify, define, formulate and solve complex computer engineering problems; for this purpose select and apply appropriate analytical and modeling methods
    5) Selects and effectively uses modern techniques and tools and information technologies required for computer science and computer engineering applications.
    6) Designs a complex computer and software based system, process, device or product to meet certain requirements under realistic constraints and conditions, including economics, environmental issues, sustainability, manufacturability, ethics, health, safety, social and political issues; For this purpose, it applies modern design methods.
    7) Has information about the standards used in computer engineering applications.
    8) Owns the competencies required by the constantly developing field of computer engineering and the global competitive environment.
    9) Acquires communication in a Foreign Language (English) competence defined on the level of at least B1 in European Language Portfolio. (In programs whose medium of instruction is English, on the level of B2/B2+).

    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
    Lectures
    Discussion
    Views
    Reading
    Peer Education
    Questions Answers
    Active Participation in Class

    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
    Presentation 1 % 10.00
    Project 1 % 15.00
    Midterms 1 % 25.00
    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 1 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 2 0 0
    Midterms 1 2 2
    Semester Final Exam 1 2 2
    Total Workload of Assesment & Evaluation Activities - - 4
    TOTAL WORKLOAD (Teaching & Learning + Assesment & Evaluation Activities) 46
    ECTS CREDITS OF THE COURSE (Total Workload/25.5 h) 7