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40
Sessions

Free
Course ( $ )

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Course Description

Digital image processing is the use of computer algorithms to perform image processing on digital images. As a subcategory or field of digital signal processing, digital image processing has many advantages over analog image processing. It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and signal distortion during processing.

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Course Syllabus
  • Lecture 1 Introduction to Digital Image Processing
  • Lecture 2 Image Digitization I
  • Lecture 3 Image Digitization II
  • Lecture 4 Pixel Relationships
  • Lecture 5 Pixels Relationships II
  • Lecture 6 Basic Transformations
  • Lecture 7 Camera Model and Imaging Geometry
  • Lecture 8 Camera Calibration and Stereo Imaging
  • Lecture -9 Interpolation and Resampling
  • Lecture - 10 Image Interpolation - II
  • Lecture - 11 Image Interpolation - I
  • Lecture - 12 Image Transformation - II
  • Lecture - 13 Fourier Transformation - I
  • Lecture - 14 Fourier Transformation - II
  • Lecture - 15 Discrete Cosine Transform
  • Lecture - 16 K-L Transform
  • Lecture - 17 Image Enhancement
  • Lecture - 18 Image Enhancement
  • Lecture - 19 Image Enhancement
  • Lecture - 20 Image Enhancement
  • Lecture - 21 Image Enhancement Frequency
  • Lecture - 22 Image Restoration - I
  • Lecture - 23 Image Restoration - II
  • Lecture - 24 Image Restoration -III
  • Lecture - 25 Image Registration
  • Lecture - 26 Colour Image Processing - I
  • Lecture - 27 Colour Image Processing - II
  • Lecture - 28 Colour Image Processing - III
  • Lecture - 29 Image Segmentation - I
  • Lecture - 30 Image Segmentation - II
  • Lecture - 31 Image Segmentation - III
  • Lecture -32 Image Segmentation - IV
  • Lecture - 33 Mathematical Morphology - I
  • Lecture - 34 Mathematical Morphology - II
  • Lecture - 35 Mathematical Morphology - III
  • Lecture - 36 Mathematical Morphology - IV
  • Lecture - 37 Object Representation and Description - I
  • Lecture - 38 Object Representation and Description - II
  • Lecture - 39 Object Representation and Description - III
  • Lecture - 40 Object Recognition
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