Eskisehir Technical University Info Package Eskisehir Technical University Info Package
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About the Program Educational Objectives Key Learning Outcomes Course Structure Diagram with Credits Field Qualifications Matrix of Course& Program Qualifications Matrix of Program Outcomes&Field Qualifications
  • Faculty Of Computer And Information Technologies
  • Artificial Intelligence And Machine Learning Pr. (English)
  • Course Structure Diagram with Credits
  • Introduction to Image Processing
  • Learning Outcomes & Program Qualifications
  • Description
  • Learning Outcomes
  • Course's Contribution to Prog.
  • Learning Outcomes & Program Qualifications
  • ECTS Credit Load
0 : Does not support   1 : Low-level support   2 : Mid-level support   3 : Top level support
Learning Outcomes123456789101112
Can read images using OpenCV and NumPy, switch between color spaces (RGB, HSV), and directly manipulate pixels.322103210112
Can calculate, interpret, and apply Histogram Equalization to image histograms.232103131112
Can manually implement 2D Convolution (filtering) using NumPy (the mathematics of the CNN layer from the ANN (Sem 5) course).333212321012
Can apply different types of noise (Gaussian, Salt-and-Pepper) and the corresponding filters (Mean, Median).233103121012
Can implement Sobel and Canny edge detectors using OpenCV and explain the effect of their parameters.233103221112
Can perform image segmentation using simple and adaptive thresholding.222203121013
Can apply morphological operations such as erosion, dilation, opening, and closing.233102121013
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