A comparison between intensity and depth images for extracting features related to wear labels in carpets

Orjuela, S. A.; Vansteenkiste, Ewout; Rooms, Filip; Meulemeester, Simon De; Keyser, Robain De; Philips, Wilfried

doi:10.1117/12.872829

Cited by 4 publications

(11 citation statements)

References 11 publications

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“…Some parts of the method have been explained in detail in previous papers [13][14][15][23][24][25][26]. In this paper we present an optimal combination of the previous findings.…”

Section: Introductionmentioning

confidence: 78%

“…We tested the algorithm using certified photographs for the American standard and photographs taken under fixed luminance conditions for the European standard. Although the method correctly distinguished AR grades in certified photographs, we found that non certified photographs are non-robust enough for describing the appearance changes of carpets [14].…”

Section: Introductionmentioning

confidence: 96%

“…The fatigued specimen samples have been collected from the following carpet types: high/low loop, loop, cut/loop, cut pile, cut design, frisé and two types of shaggy. With these fatigued specimen samples, a database consisting of both intensity and range images has been composed [14].…”

Section: Image Acquisitionmentioning

confidence: 99%

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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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“…Some parts of the method have been explained in detail in previous papers [13][14][15][23][24][25][26]. In this paper we present an optimal combination of the previous findings.…”

Section: Introductionmentioning

confidence: 78%

Section: Introductionmentioning

confidence: 96%

See 1 more Smart Citation

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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show abstract

“…2,3 Manufacturers however require a more objective assessment because the human assessment is often too subjective. Much effort has been devoted to automate this rating process, initially on techniques extracting texture features from digital images, [4][5][6][7][8][9][10][11][12] and lately combining features from digital and range images,, [13][14][15][16] where a range image represents the depth information of the surface of a carpet. The range images are obtained with a 3D scanner designed specifically for scanning carpets.…”

Section: Introductionmentioning

confidence: 99%

“…Like in a previous approach, 14 the technique has been tested in both intensity and range images from a database composed of samples from the EN1471:1996 standard. 3 The results show that the discriminance between consecutive degrees of wear increases by reducing the noise given by the patters that do not change with the wear labels.…”

mentioning

confidence: 99%

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

Vargas

Jaramillo²,

Meulemeester

et al. 2010

Advanced Concepts for Intelligent Vision Systems

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Abstract. Carpet manufacturers have wear labels assigned to their products by human experts who evaluate carpet samples subjected to accelerated wear in a test device. There is considerable industrial and academic interest in going from human to automated evaluation, which should be less cumbersome and more objective. In this paper, we present image analysis research on videos of carpet surfaces scanned with a 3D laser. The purpose is obtaining good depth images for an automated system that should have a high percentage of correct assessments for a wide variety of carpets. The innovation is the use of a wavelet edge detector to obtain a more continuously defined surface shape. The evaluation is based on how well the algorithms allow a good linear ranking and a good discriminance of consecutive wear labels. The results show an improved linear ranking for most carpet types, for two carpet types the results are quite significant.

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A comparison between intensity and depth images for extracting features related to wear labels in carpets

Cited by 4 publications

References 11 publications

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

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

Analysing wear in carpets by detecting varying local binary patterns

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

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