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Intrinsic and extrinsic active self-calibration of multi-camera systems., , and . Mach. Vis. Appl., 25 (2): 389-403 (2014)Neither Quick Nor Proper - Evaluation of QuickProp for Learning Deep Neural Networks., , , , and . CoRR, (2016)Automatic Classification of Cancerous Tissue in Laserendomicroscopy Images of the Oral Cavity using Deep Learning., , , , , , , , , and . CoRR, (2017)Detecting Regions of Maximal Divergence for Spatio-Temporal Anomaly Detection., , , and . CoRR, (2018)Active and Incremental Learning with Weak Supervision., , and . Künstliche Intell., 34 (2): 165-180 (2020)Beyond Bounding Boxes: Using Bounding Shapes for Real-Time 3D Vehicle Detection from Monocular RGB Images., , , , , and . IV, page 675-682. IEEE, (2019)On the Suitability of Different Features for Anomaly Detection in Wire Ropes., , , , and . VISIGRAPP (Selected Papers), volume 68 of Communications in Computer and Information Science, page 296-308. Springer, (2009)Extrinsic Self-Calibration of Time-of-Flight Cameras using a Combination of 3D and Intensity Descriptors., , and . VMV, page 269-276. Eurographics Association, (2011)Deep Learning on Small Datasets without Pre-Training using Cosine Loss., and . CoRR, (2019)A Comparative Evaluation of Template and Histogram Based 2D Tracking Algorithms., , , and . DAGM-Symposium, volume 3663 of Lecture Notes in Computer Science, page 269-276. Springer, (2005)