Image Analysis I: Image Processing

This lecture teaches the basic principles of low-level image analysis. "Low-level" in this case does not mean it is simple or less worthy to learn, but that we are only interested in extraction of information on a more technical level, i.e. local image structure and motion without high-level scene understanding or interpretation. Also, we will learn how to improve images by removing certain degradations like noise or blur. The range of topics includes

  • Image filtering and the Fourier transform
  • Denoising and deconvolution
  • Edge and corner detection, image features
  • Pattern recognition basics
  • Image sequences and motion analysis
  • Non-linear filters and image enhancement

Recommended pre-requisites are basic calculus and linear algebra. Basic knowledge about probability theory and statistics will be useful. MATLAB is used in the exercises, but you can learn it as the lecture proceeds.