Overview Course 2016-2017

Lecture Notes + Slides

Image Processing:
  • Images
  • Color
  • Point Operators (no isodata thresholding)
  • Geometrical Operators (including estimating of parameters for an affine or projective transformation as discussed in the separate secion on homogenuous transforms)
  • Local Operators (lecture notes + slides, read the section on bilateral filtering)
  • Local Structure (lecture notes + slides)
  • Scale-Space (lecture notes + slides, why is the Gaussian filter so important here, what is the semigroup property?)
Computer Vision:
  • The Pinhole Camera (model + calibration)
  • Motion (optic flow and normalized cross correlation)
Mathematical Tools:
  • Multivariate functions (a prerequisite for this course)
  • Linear algebra (idem)
  • Least Squares Estimators (not in full detail)
  • Homogeneous Coordinates (IMPORTANT FOR THIS COURSE)


The SIFT paper is part of the material to study for the exam.

Lab Exercises

Make sure that you understand and remember what you have done in the lab exercises. I will not ask to reproduce Python code but you can expect questions on the theory and algorithms.