Two MSc theses to be presented on June 15, 2018.
June 15 is an extra day for master thesis presentations in Computer Science at Lund University, Faculty of Engineering. Two MSc theses will be presented.
Note to potential opponents: Register as opponent to the presentation of your choice by sending an email to the examiner for that presentation (firstname.lastname@example.org). Do not forget to specify the presentation you register for! Note that the number of opponents may be limited (often to two), so you might be forced to choose another presentation if you register too late. Registrations are individual, just as the oppositions are! More instructions are found on this page.
|PRESENTERS||Linus Röman, Simon Finnman|
|TITLE||Algorithmic Approach to Error Correction in Map Datasets using Conflation Techniques|
|SUPERVISOR||Krzysztof Kuchcinski (LTH), Thomas Hermansson (ÅF - Digital Solutions AB)|
OpenStreetMap is a crowd sourced, free and open sourced data-set that contains geographical data. As any other data source OpenStreetMap contains a variety of errors, topological, geo-spatial and semantic to mention a few. This thesis focuses on using algorithms to detect and flag these errors. In particular algorithms built on the concept of conflation, eg. using another data-set in order to compare. The first step is to establish a matching between the two data-sets, which is done with an accuracy of above $94%$ in our test areas. After this match has been established differences between the data-sets can be found. These differences can be used to flag errors which can be forwarded for manual correction. We have looked at attributes such as names, where name dissimilarity can been used to differentiate different types of errors from each other, as well as investigated the correctness of speed limits in the OpenStreetMap.
|PRESENTERS||Emir Husic, Aleksandar Simeunovic|
|TITLE||Applying Object Recognition To Reduce False Alarms Triggered By Motion Detection In Video Surveillance|
|SUPERVISOR||Elin Anna Topp (LTH)|
Using motion detection in surveillance cameras is a decent way of detecting actions in environments. However, it is incapable of determining the causing source, such as animals, flying objects, or humans. This incapability tends to trigger alarms where more often then not, a human is not present.