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Sensors 2016, 16(8), 1178; doi:10.3390/s16081178

Defect Detection in Textures through the Use of Entropy as a Means for Automatically Selecting the Wavelet Decomposition Level

1
División de Sistemas e Ingeniería Electrónica (DSIE), Universidad Politécnica de Cartagena, Campus Muralla del Mar, s/n, Cartagena E-30202, Spain
2
División de Innovación en Sistemas Telemáticos y Tecnología Electrónica (DINTEL), Universidad Politécnica de Cartagena, Campus Muralla del Mar, s/n, Cartagena E-30202, Spain
These authors contributed equally to this work.
*
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 12 May 2016 / Revised: 11 July 2016 / Accepted: 22 July 2016 / Published: 27 July 2016
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [3155 KB, uploaded 27 July 2016]   |  

Abstract

This paper presents a robust method for defect detection in textures, entropy-based automatic selection of the wavelet decomposition level (EADL), based on a wavelet reconstruction scheme, for detecting defects in a wide variety of structural and statistical textures. Two main features are presented. One of the new features is an original use of the normalized absolute function value (NABS) calculated from the wavelet coefficients derived at various different decomposition levels in order to identify textures where the defect can be isolated by eliminating the texture pattern in the first decomposition level. The second is the use of Shannon’s entropy, calculated over detail subimages, for automatic selection of the band for image reconstruction, which, unlike other techniques, such as those based on the co-occurrence matrix or on energy calculation, provides a lower decomposition level, thus avoiding excessive degradation of the image, allowing a more accurate defect segmentation. A metric analysis of the results of the proposed method with nine different thresholding algorithms determined that selecting the appropriate thresholding method is important to achieve optimum performance in defect detection. As a consequence, several different thresholding algorithms depending on the type of texture are proposed. View Full-Text
Keywords: texture defect detection; wavelet transform; Shannon entropy; automatic band selection texture defect detection; wavelet transform; Shannon entropy; automatic band selection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Navarro, P.J.; Fernández-Isla, C.; Alcover, P.M.; Suardíaz, J. Defect Detection in Textures through the Use of Entropy as a Means for Automatically Selecting the Wavelet Decomposition Level. Sensors 2016, 16, 1178.

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