Fraunhofer-Institut für Optronik, Systemtechnik und Bildauswertung IOSB

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  • Publication
    Kognitive Systeme und Robotik
    ( 2018)
    Bauckhage, Christian
    ;
    Bauernhansl, Thomas
    ;
    Beyerer, Jürgen
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    Garcke, Jochen
    Kognitive Systeme können komplexe Prozesse überwachen, analysieren und gewinnen daraus auch die Fähigkeit, in ungeplanten oder unbekannten Situationen richtig zu entscheiden. Fraunhofer-Experten setzen Verfahren des maschinellen Lernens ein, um neue kognitive Funktionen für Roboter und Automatisierungslösungen zu nutzen. Dazu statten sie Systeme mit Technologien aus, die von menschlichen Fähigkeiten inspiriert sind bzw. diese imitieren und optimieren. Der Bericht beschreibt diese Technologien, erläutert aktuelle Anwendungsbeispiele und entwirft Szenarien für zukünftige Anwendungsfelder.
  • Publication
    Fast algorithm for 2D fragment assembly based on partial EMD
    ( 2017)
    Zhang, M.
    ;
    Chen, S.
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    Shu, Z.
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    Xin, S.-Q.
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    Zhao, J.
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    Jin, G.
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    Zhang, R.
    ;
    Beyerer, Jürgen
    2D Fragment assembly is an important research topic in computer vision and pattern recognition, and has a wide range of applications such as relic restoration and remote sensing image processing. The key to this problem lies in utilizing contour features or visual cues to find the optimal partial matching. Considering that previous algorithms are weak in predicting the best matching configuration of two neighboring fragments, we suggest using the earth moverâs distance, based on length/property correspondence, to measure the similarity, which potentially matches a point on the first contour to a desirable destination point on the second contour. We further propose a greedy algorithm for 2D fragment assembly by repeatedly assembling two neighboring fragments into a composite one. Experimental results on map-piece assembly and relic restoration show that our algorithm runs fast, is insensitive to noise, and provides a novel solution to the fragment assembly problem.
  • Publication
    Performance improvement of character recognition in industrial applications using prior knowledge for more reliable segmentation
    ( 2013)
    Grafmüller, M.
    ;
    Beyerer, Jürgen
    In industrial applications optical character recognition with smart cameras becomes more and more popular. Since these applications mostly have challenging environments for the systems it is most important to have very reliable character segmentation and classification algorithms. The investigations of several algorithms have shown that character segmentation is one if not the main bottleneck of character recognition. Furthermore, the requirements of robust and fast algorithms related to skew angle estimation and line segmentation, as well as tilt angle estimation, and character segmentation are high. This is the reason for introducing such algorithms that are specifically adapted to industrial applications. Additionally, a method is proposed that is based on the Bayes theorem to take account of prior knowledge for line and character segmentation. The main focus of the investigations of the character recognition system is recognition performance and speed, since real-time constraints are very hard in industrial application. Both requirements are evaluated on an image series captured with a smart camera in an industrial application.