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  • Course Information
    • Course Title Code Semester Laboratory+Practice (Hour) Pool Type ECTS
    Computer Vision BİL473 FALL-SPRING 2+0 E 4
    Learning Outcomes
    1- explain different image creation methods and different image representations.
    2-apply image transformation, enrichment and analysis algorithms.
    3- perform image conversion and analysis using Matlab / Octave programming tools.
    4- analyze vision problems with computer and develop solutions.
  • ECTS / WORKLOAD
    • ActivityPercentage

    (100)

    		NumberTime (Hours)Total Workload (hours)
    Course Duration (Weeks x Course Hours)14342
    Classroom study (Pre-study, practice)14798
    Assignments0000
    Short-Term Exams (exam + preparation) 0000
    Midterm exams (exam + preparation)3011010
    Project3011010
    Laboratory 0000
    Final exam (exam + preparation) 4011212
    Other 0000
    Total Workload (hours)   172
    Total Workload (hours) / 30 (s)     5,73 ---- (6)
    ECTS Credit   4
  • Course Content
    • Week Topics Study Metarials
    1 Introduction to Computer Vision, image modalities
    2 Image representation and introduction to Matlab/Octave
    3 Binary Images and connected component labelling algorithms, morphological operations
    4 Region properties, gray-scale, color images
    5 Geometric and intensity transformations. Histogram and histogram equalization
    6 Spatial filtering: linear and non-linear filters
    7 Edge detection
    8 Line detection, Hough transform
    9 Image projections, template matching, similarity, normalized cross correlation
    10 Fourier basis, transform, and frequency domain filtering
    11 Image pyramids, local interest points, features and matching
    12 Visual recognition, object detection
    13 Pattern recognition/classification
    14 Review
    Prerequisites -
    Language of Instruction Turkish
    Responsible Assist. Prof. Dr. Seda ŞAHİN
    Instructors -
    Assistants -
    Resources 1. LOW A., Introductory Computer Vision and Image Processing, McGrow-Hill, 1991, ENGLAND. 2. Computer vision, Linda Shapiro, George Stockman, Prentince Hall. 3.UMBAUGH S. E., Computer Vision and Image Processing, Prentice-Hall, 1998, USA.
    Supplementary Book -
    Goals to develop a computer vision application for industry by learning image processing methods. In industrial automation, computer vision is often used in areas such as counting parts, visual quality control.
    Content Introduction to Computer Vision, image modalities, Image representation and introduction to Matlab/Octave, Binary Images and connected component labelling algorithms, morphological operations, Region properties, gray-scale, color images, Geometric and intensity transformations. Histogram and histogram equalization, Spatial filtering: linear and non-linear filters, Edge detection, Line detection, Hough transform, Image projections, template matching, similarity, normalized cross correlation, Fourier basis, transform, and frequency domain filtering, Image pyramids, local interest points, features and matching, Visual recognition, object detection, Pattern recognition/classification
  • Program Learning Outcomes
    • Program Learning Outcomes Level of Contribution
    1 To be able to apply mathematics, science and engineering theories and principles to Computer Engineering problems. 4
    2 To have the ability to define, model, and solve problems related to Computer Engineering. 3
    3 To be able to design and conduct experiments, as well as to analyze and interpret data. 4
    4 To be able to design and analyze a process for a specific purpose within technical and economical limitations. 3
    5 To be able to use modern techniques and calculation tools required for engineering applications. 4
    6 To have the awareness of professional liabilities and ethics. -
    7 To be able to get involved in interdisciplined and multidisciplined team work. 3
    8 To be able to declare his/her opinions orally or written in a clear, concise and brief manner. -
    9 To improve him/herself by following the developments in science, technology, modern issues, and know the importance of lifelong learning. -
    10 To be able to evaluate engineering solutions for the global and social problems especially for the health, safety, and environmental problems. -
    11 To have knowledge about of contemporary issues. 3
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