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Methods of learning discriminative features for automated visual inspection

: Richter, M.

Fulltext urn:nbn:de:0011-n-3546444 (636 KByte PDF)
MD5 Fingerprint: b6aa2a00eb66c267f0a6e59ec9eb0aa7
Created on: 13.8.2015

Beyerer, Jürgen (Ed.); Pak, Alexey (Ed.):
Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory 2014. Proceedings : July, 20 to 26, 2014; Triberg-Nussbach in Germany
Karlsruhe: KIT Scientific Publishing, 2015 (Karlsruher Schriften zur Anthropomatik 20)
ISBN: 978-3-7315-0401-6
DOI: 10.5445/KSP/1000047712
Fraunhofer Institute of Optronics, System Technologies and Image Exploitation and Institute for Anthropomatics, Vision and Fusion Laboratory (Joint Workshop) <2014, Triberg-Nussbach>
Conference Paper, Electronic Publication
Fraunhofer IOSB ()

At the present day, automation of visual inspection tasks is a typical engineering problem. Experts design the physical aspects of the system and devise classification algorithms based on a small sample of the material to be inspected. Much of this work is devoted to finding suitable features to discriminate wanted from unwanted material. In this report, we explore methods to automatically learn object descriptors from a suitably large sample. We focus on two types of descriptors: (a) global descriptors, which represent the object as a whole and (b) local descriptors, which focus on topical features. Apart from freeing the engineers to attend to other tasks, these methods allow non-experts to operate and reuse visual inspection systems, e.g. to inspect a different product than originally intended.