Surface Reconstruction of Wear in Carpets by Using a Wavelet Edge Detector

Vargas, Sergio Alejandro Orjuela; Jaramillo, Benhur Ortiz; Meulemeester, Simon De; Alvarez, Julio César; Rooms, Filip; Pižurica, Aleksandra; Philips, Wilfried

doi:10.1007/978-3-642-17688-3_30

Cited by 5 publications

References 21 publications

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Quantifying Appearance Retention in Carpets Using Geometrical Local Binary Patterns

Quiñones

Orjuela

Ortiz-Jaramillo

et al. 2011

Advanced Concepts for Intelligent Vision Systems

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Abstract. Quality assessment in carpet manufacturing is performed by humans who evaluate the appearance retention (AR) grade on carpet samples. To quantify the AR grades objectively, different research based on computer vision have been developed. Among them Local Binary Pattern (LBP) and its variations has shown promising results. Nevertheless, the requirements of quality assessment on a wide range of carpets have not been met yet. One of the difficulties is to distinguish between consecutive AR grades in carpets. For this, we adopt an extension of LBP called Geometrical Local Binary Patterns (GLBP) that we recently proposed. The basis of GLBP is to evaluate the grey scale differences between adjacent points defined on a path in a neighbourhood. Symmetries of the paths in the GLBPs are evaluated. The proposed technique is compared with an invariant rotational mirror based LBP technique. The results show that the GLBP technique performs better to distinguish consecutive AR grades in carpets.

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Quantifying Appearance Retention in Carpets Using Geometrical Local Binary Patterns

Quiñones

Orjuela

Ortiz-Jaramillo

et al. 2011

Advanced Concepts for Intelligent Vision Systems

Self Cite

View full text Add to dashboard Cite

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Automatic grading of appearance retention of carpets using intensity and range images

Vargas

Ortiz-Jaramillo

Vansteenkiste

et al. 2012

J. Electron. Imaging

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Abstract. Textiles are mainly used for decoration and protection. In both cases, original appearance and its retention are important factors for customers. Therefore, evaluation of appearance parameters are critical for quality assurance purposes, while and after manufacturing, to determine lifetime and/or beauty of textile products. Particularly, 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. In this paper 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 materials.

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Analysing wear in carpets by detecting varying local binary patterns

et al. 2011

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Currently, carpet companies assess the quality of their products based on their appearance retention capabilities. For this, carpet samples with different degrees of wear after a traffic exposure simulation process are rated with wear labels by human experts. Experts compare changes in appearance in the worn samples to samples with original appearance. This process is subjective and humans can make mistakes up to 10% in rating. In search of an objective assessment, research using texture analysis has been conducted to automate the process. Particularly, Local Binary Pattern (LBP) technique combined with a Symmetric adaptation of the KullbackLeibler divergence (SKL) are successful for extracting texture features related to the wear labels either from intensity and range images. We present in this paper a novel extension of the LBP techniques that improves the representation of the distinct wear labels. The technique consists in detecting those patters that monotonically change with the wear labels while grouping the others. Computing the SKL from these patters considerably increases the discrimination between the consecutive groups even for carpet types where other LBP variations fail. We present results for carpet types representing 72% of the existing references for the EN1471:1996 European standard.

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Surface Reconstruction of Wear in Carpets by Using a Wavelet Edge Detector

Cited by 5 publications

References 21 publications

Quantifying Appearance Retention in Carpets Using Geometrical Local Binary Patterns

Quantifying Appearance Retention in Carpets Using Geometrical Local Binary Patterns

Automatic grading of appearance retention of carpets using intensity and range images

Analysing wear in carpets by detecting varying local binary patterns

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