Wavelet-based Defect Detection System for Grey-level Texture Images

Gintarė Vaidelienė, Jonas Valantinas


In this study, a new wavelet-based approach (system) to the detection of defects in grey-level texture images is presented. This new approach explores space localization properties of the discrete wavelet transform (DWT) and generates statistically-based parameterized defect detection criteria. The introduced system’s parameter provides the user with a possibility to control the percentage of both the actually defect-free images detected as defective and/or the actually defective images detected as defect-free, in the class of texture images under investigation. The developed defect detection system was implemented using discrete Haar and Le Gall wavelet transforms. For the experimental part, samples of ceramic tiles, as well as glass samples, taken from real factory environment, were used.


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Paper Citation

in Harvard Style

Vaidelienė G. and Valantinas J. (2016). Wavelet-based Defect Detection System for Grey-level Texture Images . In Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016) ISBN 978-989-758-175-5, pages 143-149. DOI: 10.5220/0005678901430149

in Bibtex Style

author={Gintarė Vaidelienė and Jonas Valantinas},
title={Wavelet-based Defect Detection System for Grey-level Texture Images},
booktitle={Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)},

in EndNote Style

JO - Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, (VISIGRAPP 2016)
TI - Wavelet-based Defect Detection System for Grey-level Texture Images
SN - 978-989-758-175-5
AU - Vaidelienė G.
AU - Valantinas J.
PY - 2016
SP - 143
EP - 149
DO - 10.5220/0005678901430149