Basic mathematical concepts: Image transforms, DFT (Discrete Fourier Transform), FFT (Fast Fourier Transform).
Image enhancement: Grey level transforms, filtering.
Image restoration: Filterings, inverse methods.
Scale space theory: Continuous versus discrete theory, interpolation.
Extraction of special features: Filtering, edge and corner detection.
Segmentation: graph-methods, active contours, mathematical morphology.
Bayesian image handling: MAP(Maximum Aposteriori) estimations, simulation.
Pattern recognition: Classification, SVM (Support Vector Machines), PCA (Principal Component Analysis), learning.
Machine Learning: Training, testing, generalization, hypothesis spaces.