Statistical Analysis, Machine Learning and Image Analysis - SAMBA
The SAMBA department has comprehensive theoretical and practical knowledge in the fields of statistics, machine learning and image analysis. We are one of Europe's largest and most competent groups within applied statistics and statistical-matematical modelling. We cover a broad spectrum of methods and are a world leader in some of these areas. The appropriate choice of method for the various problems is thus one of our strengths. Many calculations involve uncertainty and the accurate calculation of this quantity is an important speciality.
Last 5 scientific articles
Melsheimer, Christian; Mäkynen, Marko; Rasmussen, Till Andreas Soya; Rudjord, Øystein; Similä, Markku H.; Solberg, Rune; Walker, Nick P.. Comparison and validation of four Arctic Sea ice thickness products of the EC POLAR ICE project. ESA SP (ISSN 0379-6566). SP-740 2016.
Kampffmeyer, Michael C.; Salberg, Arnt-Børre; Jenssen, Robert. Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks. IEEE Computer Society Conference on Computer Vision and Pattern Recognition workshops (ISSN 2160-7508). pp 680-688. doi: 10.1109/CVPRW.2016.90. 2016.
Halvorsen, Sigrun; Ghanima, Waleed Khalid; Tvete, Ingunn Fride; Hoxmark, Cecilie; Falck, Pål; Solli, Oddvar; Jonasson, Christian. A nationwide registry study to compare bleeding rates in patients with atrial fibrillation being prescribed oral anticoagulants. European Heart Journal - Cardiovascular Pharmacotherapy (ISSN 2055-6837). 3(1) pp 28-36. doi: 10.1093/ehjcvp/pvw031. 2016. Full-text
Lison, Pierre; Meena, Raveesh. Automatic Turn Segmentation of Movie and TV Subtitles. In: 2016 Spoken Language Technology Workshop. (ISBN 978-1-5090-4902-8). pp 245-252. doi: 10.1109/SLT.2016.7846272. 2016.