Bachelor (Kexjobb) and Master (Exjobb) Project Positions at CVAP
We host a number of Master students each year, who perform research-related projects during 5 months. We also host groups of Bachelor students carrying out smaller projects. Below, we list open project positions. Each position is indicated as suitable as a Master project, Bachelor project, or both. If you do not find a project that suits you, contact your favorite CVAP faculty member to tailor a project.
Periodic Motion Detection and Interpolation
Contact: Florian Pokorny, fpokorny@kth.se or Mikael Vejdemo-Johansson, mvj@kth.se
Suitable as: Master Project
This project will investigate how periodic motion capture sequences of a human gait can be interpolated. This work will be based on the recent paper [our SIGGRAPH submission] and one of the aims could be to implement a suitable smoothing algorithm using e.g. Gaussian Process regression. Periodic motion interpolation is a problem in character animation in the games and film industry. Problems that the student could attack include questions such as: How do we smoothly change an animation of a character from running to walking? How do we interpolate between $n$ styles of running simultaneously? How can we smooth the motion data correctly?
Periodic Dynamical Systems
Contact: Florian Pokorny, fpokorny@kth.se or Mikael Vejdemo-Johansson, mvj@kth.se
Suitable as: Master Project
The student will investigate how Persistent Homology and Cohomology can be used to study a particular dynamical system of interest.
Art Gallery Problem
Contact: Florian Pokorny, fpokorny@kth.se or Mikael Vejdemo-Johansson, mvj@kth.se
Suitable as: Master Project
How can we position n sensors with infinite range to optimally observe
a fixed polygonal object? This problem, which is also known as the Art
gallery problem lies at the intersection of computational topology and
robotics. Several questions related to this problem are unsolved, such
as the problem of representing uncertainty. How do we place these
sensors if we only have a Bayesian estimate of the position of the
polygonal object? How do we deal with noise in the position of the object?
Local Image Descriptors for Image Matching and Object Recognition
Contact: Oskar Linde, ol@csc.kth.se or Tony Lindeberg, tony@csc.kth.se
Suitable as: Master Project
A common approach to image matching and object recognition consists of detecting interest points and computing image descriptors at these points. Currently, the SIFT and SURF descriptors constitute the most frequently used image descriptors with a large number successful applications in computer vision and related fields. From local image measurements, it is however possible to compute other types of image descriptors that may lead to better performance for image matching and/or object recognition.
The subject of this MSc thesis project is to investigate, compare and if possible extend two recently developed approaches for computing local image descriptors from local image information, one based on Gaussian derivatives in terms of the local N-jet and one approach inspired by linear and non-linear receptive fields in biological vision. The formulation of this project is near basic research issues and opens up the possibility of comparing approaches in computer vision to computational approaches inspired by biological vision.
