Seeing is believing and humans rely on their sight to make decisions. As a result, a lot of data we gather in various ways is visual. It is natural to want to get the most out of the data we have and one method toward achieving this is semantic segmentation. In essence, semantic segmentation means figuring out which pixels belong to which type of object. For example, how can a machine recognize a pedestrian on a road crossing or differentiate between fields and swamplands? Deep Learning has helped make huge strides in the field of semantic segmentation but there are still many challenges to overcome!
📍01:55 Dmytro Fishman - "Biomedical segmentation: holy grail and remaining mysteries"
📍27:41 Tanya Shtym - "KappaMask: AI-Based Cloud Mask Processor for Sentinel 2"
📍52:05 Markus Kängsepp - "Calibration of Bird's-Eye-View Semantic Segmentation"
📍1:16:05 Kaupo Voormansik - "Data Science and Machine Learning in KappaZeta"
📍1:49:32 Joint panel discussion
More information about our seminars: https://cs.ut.ee/en/content/data-science-seminars