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Pattern Analysis and Applications

Erschienen in:

01.08.2013 | Short Paper

Image-based supervision of a periodically working machine

verfasst von: Mario Frank, Fred A. Hamprecht

Erschienen in: Pattern Analysis and Applications | Ausgabe 3/2013

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Abstract

Most industrial robots perform a periodically repeating choreography. Our aim is to detect disturbances of such a periodic process by a visual inspection system that can be trained with a minimum of human effort and interaction. We present a solution that monitors the robot with a time-of-flight 3D camera. Our system can be trained using a few unperturbed cycles of the periodic process. More specifically, principal components are used to find a low-dimensional approximation of each frame, and a One-Class Support Vector Machine is used for one-class learning. We propose a novel scheme for automatic parameter tuning, which exploits the fact that successive images of the training class should be close in feature space. We present exemplary results for a miniature robot setup. The proposed strategy does not require prior information on the dimensions of the machine or its maneuvering range. The entire system is appearance-based and hence does not need access to the robot’s internal coordinates.

Vorheriger Artikel Variable subspace model for hyperspectral anomaly detection

Nächster Artikel Classification using distances from samples to linear manifolds

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Titel: Image-based supervision of a periodically working machine
verfasst von: Mario Frank
Fred A. Hamprecht
Publikationsdatum: 01.08.2013
Verlag: Springer London
Erschienen in: Pattern Analysis and Applications / Ausgabe 3/2013
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI: https://doi.org/10.1007/s10044-011-0245-7

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