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Authors: Esther-Sabrina Platzer 1 ; Joachim Denzler 1 ; Herbert Süße 1 ; Josef Nägele 2 and Karl-Heinz Wehking 2

Affiliations: 1 Friedrich Schiller University of Jena, Germany ; 2 University of Stuttgart, Germany

ISBN: 978-989-8111-69-2

Keyword(s): Anomaly detection, Novelty detection, One-class classification, Linear prediction, Local binary pattern.

Related Ontology Subjects/Areas/Topics: Computer Vision, Visualization and Computer Graphics ; Feature Extraction ; Features Extraction ; Image and Video Analysis ; Informatics in Control, Automation and Robotics ; Signal Processing, Sensors, Systems Modeling and Control ; Statistical Approach

Abstract: Automatic visual inspection of wire ropes is an important but challenging task. Anomalies in wire ropes usually are unobtrusive and their detection is a difficult job. Certainly, a reliable anomaly detection is essential to assure the safety of the ropes. A one-class classification approach for the automatic detection of anomalies in wire ropes is presented. Different well-established features from the field of textural defect detection are compared to context-sensitive features extracted by linear prediction. They are used to learn a Gaussian mixture model which represents the faultless rope structure. Outliers are regarded as anomaly. To evaluate the robustness of the method, a training set containing intentionally added, defective samples is used. The generalization ability of the learned model, which is important for practical life, is exploited by testing the model on different data sets from identically constructed ropes. All experiments were performed on real-life rope data. Th e results prove a high generalization ability, as well as a good robustness to outliers in the training set. The presented approach can exclude up to 90 percent of the rope as faultless without missing one single defect. (More)

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Paper citation in several formats:
Platzer E.; Denzler J.; Süße H.; Nägele J.; Wehking K. and (2009). ROBUSTNESS OF DIFFERENT FEATURES FOR ONE-CLASS CLASSIFICATION AND ANOMALY DETECTION IN WIRE ROPES.In Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009) ISBN 978-989-8111-69-2, pages 170-177. DOI: 10.5220/0001783801700177

@conference{visapp09,
author={Esther{-}Sabrina Platzer and Joachim Denzler and Herbert Süße and Josef Nägele and Karl{-}Heinz Wehking},
title={ROBUSTNESS OF DIFFERENT FEATURES FOR ONE-CLASS CLASSIFICATION AND ANOMALY DETECTION IN WIRE ROPES},
booktitle={Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)},
year={2009},
pages={170-177},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001783801700177},
isbn={978-989-8111-69-2},
}

TY - CONF

JO - Proceedings of the Fourth International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2009)
TI - ROBUSTNESS OF DIFFERENT FEATURES FOR ONE-CLASS CLASSIFICATION AND ANOMALY DETECTION IN WIRE ROPES
SN - 978-989-8111-69-2
AU - Platzer, E.
AU - Denzler, J.
AU - Süße, H.
AU - Nägele, J.
AU - Wehking, K.
PY - 2009
SP - 170
EP - 177
DO - 10.5220/0001783801700177

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