14 May 2012 Automatic grading of appearance retention of carpets using intensity and range images
Sergio Orjuela Vargas, Ewout Vansteenkiste, Filip Rooms, Wilfried Philips, Benhur Ortiz-Jaramillo, Simon De Meulemeester, Lieva Van Langenhove, Robain De Keyser
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Abstract
Textiles are mainly used for decoration and protection. In both cases, their original appearance and its retention are important factors for customers. Therefore, evaluation of appearance parameters are critical for quality assurance purposes, during and after manufacturing, to determine the lifetime and/or beauty of textile products. In particular, appearance retention of textile products is commonly certified with grades, which are currently assigned by human experts. However, manufacturers would prefer a more objective system. We present an objective system for grading appearance retention, particularly, for textile floor coverings. Changes in appearance are quantified by using linear regression models on texture features extracted from intensity and range images. Range images are obtained by our own laser scanner, reconstructing the carpet surface using two methods that have been previously presented. We extract texture features using a variant of the local binary pattern technique based on detecting those patterns whose frequencies are related to the appearance retention grades. We test models for eight types of carpets. Results show that the proposed approach describes the degree of wear with a precision within the range allowed to human inspectors by international standards. The methodology followed in this experiment has been designed to be general for evaluating global deviation of texture in other types of textiles, as well as other surface
© 2012 SPIE and IS&T 0091-3286/2012/$25.00 © 2012 SPIE and IS&T
Sergio Orjuela Vargas, Ewout Vansteenkiste, Filip Rooms, Wilfried Philips, Benhur Ortiz-Jaramillo, Simon De Meulemeester, Lieva Van Langenhove, and Robain De Keyser "Automatic grading of appearance retention of carpets using intensity and range images," Journal of Electronic Imaging 21(2), 021106 (14 May 2012). https://doi.org/10.1117/1.JEI.21.2.021106
Published: 14 May 2012
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CITATIONS
Cited by 3 scholarly publications and 1 patent.
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KEYWORDS
Autoregressive models

Photography

Standards development

Binary data

Feature extraction

Scanners

RGB color model

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