Master thesis – Synthetic training images for Machine Learning i Lund

Om tjänsten

Category
Master of Science in applied mathematics; computer science or similar

Scope
2 students completing 30 credits (20 weeks) each.

Educational Background
Master of Science in applied mathematics, computer science or similar. Understanding of how a Linux system works and experience from courses in machine learning is required. Programming experience in, Python and C/C++ is meriting.

Background
Deep learning is a type of machine learning involving neural networks in a way which is loosely inspired by the way the brain processes information. This branch of machine learning has in the past few years had an amazing success, creating a huge boom which is currently transforming the way entire businesses work.

In the area of video surveillance, deep learning is an enabler for autonomous, or semi-autonomous surveillance systems, relying less and less on the constant monitoring of human operators. Typical algorithms that now predominantly start relying on deep learning are object classification, detection and tracking, and various forms of identification or re-identification algorithms.

However, one of the biggest challenges when working with machine learning is that such algorithms are extremely data hungry, often requiring millions of annotated training images to be used for so-called supervised learning. The task of creating such datasets and annotating them requires an enormous effort that often cannot be automated.

Goal
In addition to synthetic data being completely free from data integrity related issues, it also makes it possible to get the annotation almost for free, since all of the information about the scene is already known by the computer engine generating the scene. This opens up for the possibility of using more advanced algorithms working in the time-domain rather than just analyzing still images.

There are at least three possible main goals for this thesis:
The first goal is to find out how good results one can get by generating large amounts of artificial training data and training a machine learning algorithm using this data. The second goal is to enhance the training data further using Deep Learning methods. The third goal is to analyze temporal algorithms which typically benefit from even larger variation in the training data than is possible today by using real-world video only to train on.

OK, I am interested! What do I do now?
You are valuable to us – how nice that you are interested in one of our proposals! There are a few things for you to keep in mind when applying.

• Applications are accepted in both Swedish and English, and you apply via the proposal advert.
• The announced theses are open only to students affiliated with a Swedish university/college either directly or via an exchange program.
• When the thesis proposal states that it includes two students working together, we would like you to apply in pairs. In these cases, send one application each but make sure to clearly state in your application who your co-applicant is. If you have any questions regarding this, please do not hesitate to contact us.
• Please attach your CV and University/college grade summary. 

We look forward to hearing from you!

Extra information

Status

Stängd

Ort

Lund

Typ av kontrakt

Examensarbete

Typ av jobb

IT

Körkort önskas

Nej

Tillgång till bil önskas

Nej

Personligt brev krävs

Nej