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Authors: Paul Bergmann 1 ; Sindy Löwe 2 ; Michael Fauser 1 ; David Sattlegger 1 and Carsten Steger 1

Affiliations: 1 MVTec Software GmbH, Germany ; 2 MVTec Software GmbH, Germany, University of Amsterdam and The Netherlands

ISBN: 978-989-758-354-4

Keyword(s): Unsupervised Learning, Anomaly Detection, Defect Segmentation.

Abstract: Convolutional autoencoders have emerged as popular methods for unsupervised defect segmentation on image data. Most commonly, this task is performed by thresholding a per-pixel reconstruction error based on an p̀-distance. This procedure, however, leads to large residuals whenever the reconstruction includes slight localization inaccuracies around edges. It also fails to reveal defective regions that have been visually altered when intensity values stay roughly consistent. We show that these problems prevent these approaches from being applied to complex real-world scenarios and that they cannot be easily avoided by employing more elaborate architectures such as variational or feature matching autoencoders. We propose to use a perceptual loss function based on structural similarity that examines inter-dependencies between local image regions, taking into account luminance, contrast, and structural information, instead of simply comparing single pixel values. It achieves significant pe rformance gains on a challenging real-world dataset of nanofibrous materials and a novel dataset of two woven fabrics over state-of-the-art approaches for unsupervised defect segmentation that use per-pixel reconstruction error metrics. (More)

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Paper citation in several formats:
Bergmann, P.; Löwe, S.; Fauser, M.; Sattlegger, D. and Steger, C. (2019). Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders.In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP, ISBN 978-989-758-354-4, pages 372-380. DOI: 10.5220/0007364503720380

@conference{visapp19,
author={Paul Bergmann. and Sindy Löwe. and Michael Fauser. and David Sattlegger. and Carsten Steger.},
title={Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,},
year={2019},
pages={372-380},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007364503720380},
isbn={978-989-758-354-4},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 5: VISAPP,
TI - Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders
SN - 978-989-758-354-4
AU - Bergmann, P.
AU - Löwe, S.
AU - Fauser, M.
AU - Sattlegger, D.
AU - Steger, C.
PY - 2019
SP - 372
EP - 380
DO - 10.5220/0007364503720380

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