GeMMa Laboratory

GeMMa Laboratory is rated 3 out of 5 in the category research. Read and write reviews about GeMMa Laboratory. The Laboratory for Geometric Modelling and Multimedia Algorithms acts within the Faculty of Electrical Engineering and Computer Science at the University of Maribor, Slovenia, as one of the largest laboratories. There are currently 26 full-time members (10 PhDs) and 2 part-time members (both PhDs): 8 university teachers, and the rest are teaching assistants, researchers or technical assistants. Starting with development, implementation and improvements of fundamental geometric algorithms in 2000, GeMMA gradually gained expertise in geographic information systems (GIS), spatial data processing (data fusion, compression, filtering, feature extraction), data analytics, and pattern analysis, while its reunion (2015) with the Laboratory for Computer Graphics and Artificial Intelligence upgraded its skills in artificial intelligence, data mining, modelling and visualization. GeMMA is currently among the world’s leading research groups in geometric pattern recognition, recognised by the International Society of Photogrammetry and Remote Sensing for the most efficient algorithms (at their issue date) for object extraction and 3D reconstruction from huge remotely sensed point-clouds. It is also actively collaborating with the industry in development of GeoServer and its deployment to various geographic information systems, including National Geographic Infrastructure of Slovenia, Croatia, and Macedonia. In the latest years, the research activities in GeMMA are focussed to the following fields and topics: • Earth observation data processing (data compression; feature classification and extraction; visualization; environmental simulations, data modelling, and applications); • artificial intelligence, machine learning, big data, and complex networks; • geometric modelling, animation, virtual and augmented reality; • data compression (LiDAR, rasterized objects, video, medical images); • data segmentation, clustering, and communities; • medical applications • GPU programming