Defect detection in textures through the use of entropy as a means for automatically selecting the wavelet decomposition level
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Navarro Lorente, Pedro Javier; Fernández Isla, Carlos; Alcover Garau, Pedro María; Suardíaz Muro, JuanÁrea de conocimiento
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The work submitted here has been conducted within the framework of the ViSel-TR project (Selective Computer Vision Techniques for Non-structured Environments, Grant TIN2012-39279) funded by the Spanish government under the National Plan for R&D. This article is the result of the activity carried out under the “Research Programme for Groups of Scientific Excellence at Region of Murcia” of the Seneca Foundation (Agency for Science and Technology of the Region of Murcia—19895/GERM/15).Fecha de publicación
2016-07Editorial
MDPICita bibliográfica
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. https://doi.org/10.3390/s16081178Revisión por pares
siPalabras clave
texture defect detectionwavelet transform
Shannon entropy
automatic band selection
Resumen
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 ...
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