Schack, T. (2022): Bildbasierte Frischbetonprüfung zur digitalen Qualitätsregelung,  Berichte aus dem Institut für Baustoffe, Gottfried Wilhelm Leibniz Universität Hannover Weitere Informationen
DOI: https://doi.org/10.15488/13180
ISBN: 978-3-936634-27-3

Coenen, M.; Schack, T.; Beyer, D.; Heipke, C.; Haist, H. (2022): ConsInstancy: Learning Instance Representations for Semi-Supervised Panoptic Segmentation of Concrete Aggregate Particles,  Machine Vision and Applications 33 (57)
DOI: https://doi.org/10.1007/s00138-022-01313-x

Coenen, M.; Beyer, D.; Heipke, C.; Haist, M. (2022): Learning to Sieve: Prediction of Grading Curves from Images of Concrete Aggregate,  ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2022 Weitere Informationen
DOI: 10.5194/isprs-annals-V-2-2022-227-2022

Haist, M.; Heipke, C.; Beyer, D.; Coenen, M.; Schack, T.; Vogel, C.; Ponick, A.; Langer, A. (2022): Digitization of the Concrete Production Chain Using Computer Vision and Artificial Intelligence,  Proceedings of the 6th fib Congress 2022, Oslo, Norway. Weitere Informationen

Ponick, A.; Langer, A.; Beyer, D.; Coenen, M.; Haist, M.; Heipke, C. (2022): Image-based Deep Learning for Rheology Determination of Bingham Fluids,  International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2022, pp. 711–720 Weitere Informationen
DOI: 10.5194/isprs-archives-XLIII-B2-2022-711-2022

Coenen, M.; Schack, T.; Beyer, D.; Heipke, C.; Haist, M. (2021): Semi-supervised segmentation of concrete aggregate using consensus regularisation and prior guidance,  ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2-2021, 83–91 Weitere Informationen
DOI: https://doi.org/10.5194/isprs-annals-V-2-2021-83-2021

Haist, M.; Scheydt, J.; Heipke, C.; Beyer, D.; Coenen, M.; Schack, T.; Ponick, A.; et al. (2022): Concrete 4.0 – Self-learning digital production techniques for sustainable concrete,  BFT International, 06/2022. S. 61-62

Schack, T.; Coenen, M.; Haist, M. (2022): Slump Flow 4.0 - Digital quality control,  BFT International, 06/2022. S. 73

Haist, M.; Heipke, C.; Schack, T. (2021): Computer Vision-basierte Messmethoden zur Charakterisierung von (Frisch-)Betoneigenschaften,  DBV-Rundschreiben 269, S. 24-27