Advanced Image Analysis

• Implement advanced image analysis algorithms in Python.
• 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 machine learning methods/neural networks to image analysis problems.
• Apply scale space methods, and know when this is appropriate.
• Apply feature based methods to solve image analysis problems
• Apply deformable template models, and estimate these from data.
• Apply Markov Random Field techniques to image analysis problems.

The course introduces advanced topics within image analysis, focusing on a fundamental understanding of the included image analysis techniques. Therefore, the focus of the exercises is on implementing algorithms in Python and using these for solving practical image analysis problems within the following topics: Detection of image features, scale space models, texture characterization and modeling, Markov models, neural networks, shape models, and other similar image analysis methods. Students become capable of identifying methods for solving image analysis problems, setting up test scenarios for implementing and verifying image analysis methods, and carrying out and reporting quantitative analysis.

02502/02512, Introductory course in image analysis, e.g. 02502, 02511 or 02512.

Lectures and computer exercises.