@misc{EltaherBreuss, author = {Eltaher, Mahmoud and Breuß, Michael}, title = {Unsupervised Description of 3D Shapes by Superquadrics Using Deep Learning}, series = {Computer Vision and Machine Intelligence : Proceedings of CVMI 2022}, journal = {Computer Vision and Machine Intelligence : Proceedings of CVMI 2022}, publisher = {Springer}, address = {Singapore}, isbn = {978-981-19-7866-1}, issn = {978-981-19-7867-8}, doi = {10.1007/978-981-19-7867-8_9}, pages = {95 -- 107}, abstract = {The decomposition of 3D shapes into simple yet representative components is a very intriguing topic in computer vision as it is very useful for many possible applications. Superquadrics may be used with benefit to obtain an implicit representation of the 3D shapes, as they allow to represent a wide range of possible forms by few parameters. However, in the computation of the shape representation, there is often an intricate trade-off between the variation of the represented geometric forms and the accuracy in such implicit approaches. In this paper, we propose an improved loss function, and we introduce beneficial computational techniques. By comparing results obtained by our new technique to the baseline method, we demonstrate that our results are more reliable and accurate, as well as much faster to obtain.}, language = {en} } @misc{BonhageEltaherRaabetal., author = {Bonhage, Alexander and Eltaher, Mahmoud and Raab, Thomas and Breuß, Michael and Raab, Alexandra and Schneider, Anna}, title = {A modified Mask region-based convolutional neural network approach for the automated detection of archaeological sites on high-resolution light detection and ranging-derived digital elevation models in the North German Lowland}, series = {Archaeological Prospection}, volume = {28}, journal = {Archaeological Prospection}, number = {2}, issn = {1099-0763}, doi = {10.1002/arp.1806}, pages = {177 -- 186}, language = {en} } @misc{EltaherBonhageRaabetal., author = {Eltaher, Mahmoud and Bonhage, Alexander and Raab, Thomas and Breuß, Michael}, title = {A modified Mask R-CNN approach for automated detection of archaeological sites on high resolution LiDAR-derived DEMs}, series = {Anthropogenetische Geomorphologie - Geomorophologie im Anthropoz{\"a}n : Virtuelle Jahrestagung des Arbeitskreises f{\"u}r Geomorphologie 2020, 28./29. September 2020, BTU Cottbus - Senftenberg}, journal = {Anthropogenetische Geomorphologie - Geomorophologie im Anthropoz{\"a}n : Virtuelle Jahrestagung des Arbeitskreises f{\"u}r Geomorphologie 2020, 28./29. September 2020, BTU Cottbus - Senftenberg}, publisher = {Cottbus}, issn = {2196-4122}, doi = {10.26127/BTUOpen-5363}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:co1-opus4-53633}, pages = {50}, language = {en} }