Overview Course 2016-2017
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Lecture Notes + Slides
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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)


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

Lab Exercises
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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.