@inproceedings{SchollBartellaMoluluoetal.2019, author = {Scholl, Ingrid and Bartella, Alexander K. and Moluluo, Cem and Ertural, Berat and Laing, Frederic and Suder, Sebastian}, title = {MedicVR : Acceleration and Enhancement Techniques for Direct Volume Rendering in Virtual Reality}, series = {Bildverarbeitung f{\"u}r die Medizin 2019 : Algorithmen - Systeme - Anwendungen}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2019 : Algorithmen - Systeme - Anwendungen}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-25326-4}, doi = {10.1007/978-3-658-25326-4_32}, pages = {152 -- 157}, year = {2019}, language = {en} } @article{SchmittSchollCaietal.2010, author = {Schmitt, Robert and Scholl, Ingrid and Cai, Yu and Xia, Ji and Dziwoki, Paul and Harding, Martin and Pavim, Alberto}, title = {Machine vision system for inline inspection in carbide insert production}, series = {Proceedings of the 36th International MATADOR Conference}, journal = {Proceedings of the 36th International MATADOR Conference}, publisher = {Springer}, address = {Berlin}, isbn = {978-1-84996-431-9}, doi = {10.1007/978-1-84996-432-6_77}, pages = {339 -- 342}, year = {2010}, abstract = {In steps of the production chain of carbide inserts, such as unloading or packaging, the conformity test of the insert type is done manually, which causes a statistic increase of errors due to monotony and fatigue of the worker and the wide variety of the insert types. A machine vision system is introduced that captures digital frames of the inserts in the production line, analyses inspects automatically and measures four quality features: coating colour, edge radius, plate shape and chip-former geometry. This new method has been tested on several inserts of different types and has shown that the prevalent insert types can be inspected and robustly classified in real production environment and therefore improves the manufacturing automation.}, language = {en} }