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pro vyhledávání: '"Michael Rubik"'
Publikováno v:
Pattern Recognition Letters. 26:61-75
We present a vision-based approach to coin classification which is able to discriminate between hundreds of different coin classes. The approach described is a multistage procedure. In the first stage a translationally and rotationally invariant desc
Publikováno v:
IFAC Proceedings Volumes. 37:507-512
For the exact sorting of more than 1000 different categories of coins with almost 100 percent of recognition it was necessary to develop a unique high-speed coin sorting machine called “Dagobert” by ARC Seibersdorf research GmbH. Based on optical
Autor:
Michael Hödlmoser, Reinhold Huber-Mörk, Michael Rubik, Michael Nölle, Sebastian Zambanini, Martin Kampel
Publikováno v:
Advances in Object Recognition Systems
We investigate object recognition and classification in a setting with a large number of classes as well as recognition and identification of individual objects of high similarity. Real-world data sets were obtained for the classification and identif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::b4de9b8163ca20352674e733fe87d5ec
https://doi.org/10.5772/35795
https://doi.org/10.5772/35795
Autor:
Johannes Fürtler, Christian Eckel, Ernst Bodenstorfer, Jörg Brodersen, Michael Rubik, Konrad Mayer
Publikováno v:
Smart Cameras ISBN: 9781441909527
This chapter describes a high performance smart linescan camera developed to be used in quality inspection systems for high grade printed matter. Such an inspection system has to meet many demanding requirements as very high inspection resolution (be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6bcd6e29e7c8e6582c3f61ed2fc739df
https://doi.org/10.1007/978-1-4419-0953-4_7
https://doi.org/10.1007/978-1-4419-0953-4_7
Autor:
Michael Rubik, Johannes Fuertler, Harald Penz, Konrad Mayer, Herbert Nachtnebel, Joerg Brodersen, Christian Eckel, Gemeiner Christian
Publikováno v:
Real-Time Image Processing
For industrial print flaw detection images are acquired and then compared to a specimen (master image). Due to the production process, the images are not exactly aligned to each other. Therefore, preceding a pixel-by-pixel comparison, the acquired im