Oliver Kahmen, M.Sc.

Provenance and Collection Research Digital
mail:
tel: +49 (0) 441 - 7708 - 3349
room: G 207

Books and Papers

Rofallski, R.; Kahmen, O.; Luhmann, T. (2022): Investigating distance-dependent distortion in multimedia photogrammetry for flat refractive interfaces. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLVIII-2/W2-2022, 127–134 , doi: 10.5194/isprs-archives-XLVIII-2-W2-2022-127-2022 , Weblink
Luhmann, T.; Rofallski, R.; Kahmen, O. (2022): Möglichkeiten und Grenzen der hochgenauen photogrammetrischen Objekterfassung unter Wasser. DVW/DHyG Fachtagung "Hydrographie - Messen mit allen Sinnen", Schriftenreihe des DVW, Band 102, pp. 109-116
Kahmen, O.; Luhmann, T. (2022): Monocular Photogrammetric System for 3D Reconstruction of Welds in Turbid Water. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science, Vol. 90, Issue 1, pp. 19-35 , doi: 10.1007/s41064-022-00191-2

Presentations

Kahmen, O. : Influence of Quantity, Size and Arrangement of Scale Bars in Large Volume Photogrammetry. EPMC European Portable Metrology Conference 2015, Manchester, Oktober 2015

Projects

funded by: zukunft.niedersachsen
There are hundreds of thousands of artefacts in German museums that were brought to Europe during the colonial era as a result of wars, looting or trade. The history of these objects - where they came from, what they were used for and who once owned ... more
funded by: Federal Ministry for Economic Affairs and Climate Action
The goal of the project is to supplement the classic visual inspection (VT) of welded joints under water with an optical 3D measurement system. Based on high-resolution 2D (image) data, metric 3D (surface) data will be generat... more
funded by: Bundesministerium für Bildung und Forschung
The Project – „CoSAIR – Collaborative Spatial Artificial Intelligence in Realtime“ is funded by the Federal Ministry of Education and Research within the program „Research at Universities of Applied Sciences“ in order to create, consolida... more

Bachelor & Master Theses


Bildbasierte Detektion von Rissen in Schweißverbindungen mit den Methoden des Deep Learnings (2022/10)
Untersuchung verschiedener Bildanalyseverfahren zur Orientierung von Bildsequenzen eines Dreikamerasystems (2020/2)
Entwicklung eines Prozesses zur Detektion von Rissen auf Schweißnähten durch digitale Bildverarbeitung (2020/1)
Untersuchung von Orientierungs- und Matchingverfahren für die hochgenaue 3D-Oberflächenerfassung von Schweißnähten mit einem mobilen Kamerasystem (2018/9)