Lectures, paper and pen exercises, matlab exercises, assignments.
Kursets varighed:
13-uger
Evalueringsform:
Bedømmelsesform:
Overordnede kursusmål:
To give the students a foundation in the mathematical tools of bayes inference, variational formulations and partial differential equations all with the specific target of applications within image analysis. The framework of level sets will also be introduced.
The student will be able to explain the methods within bayes inference, variational energy formulation and partial differential equations applied to the field of image analysis.
The student will be gain knowledge on the mathematics upon which the methods are based.
The student will gain sufficient numeric insight to implement selected PDE's among the classical ones such as curvature flow and anisotropic diffusion.
The student will be able to implement a few of the more advanced PDE solutions to image analysis problems such as tracking and "hole" filling in images.
The student will gain indepth knowledge on the level set implementation of curve flows. From basic theory to full implementations.
Kursusindhold:
The course will cover differential geometry for planar curves, differential geometry for simple surfaces given as graphs or implicit defined, variational energy formulation of classical tasks such as segmentation as well as their connection and deriviation from bayes inference, the minimization of these via partial differential equations, the issues of statistics of natural images as well as statistics of simple transformations will be briefly introduced. These theoretical issues will explain using practical examples. The students will have to implement several of the techniques during the course.
