CANKIRI KARATEKIN UNIVERSITY Bologna Information System


  • 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.
    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
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