To give knowledge of advanced statistical methods and models for analyzing image data, and give competence in applying these techniques in different applications. The course attempts to make the participants recognize that the use of appropriate statistical models can extract useful knowledge from image data - knowledge that is not directly accessible.

• Implement advanced image analysis algorithms in MatLab.
• Assess if an implemented image analysis algorithm works correctly and gives the desired results.
• Motivate and identify the underlying assumptions of an image analysis method.
• Apply Baysian methods to image analysis problems.
• Apply scale space methods, and know when this is appropriate.
• Apply texture analysis on image analysis problems
• Apply deformable template models, and estimate these from data.
• Apply Markov Random Field techniques to image analysis problems.
• Apply multiple view geometry methods to image analysis problems.
• Object recognition and image search.

Markov random fields, conditional and simultaneous distributions, simulation and estimation, Bayesian image analysis, orthogonal transformations of multispectral images, texture modeling, geostatistical models, contextual classification methods.