# 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)

## Papers¶

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.