Feature extraction of the wear label of carpets by using a novel 3D scanner

Orjuela, S. A.; Vansteenkiste, Ewout; Rooms, Filip; Meulemeester, Simon De; Keyser, Robain De; Phillips, William J.

doi:10.1117/12.853853

Cited by 9 publications

(15 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These photographs were converted from the RGB-model to the YUV-model, with values from 1 to 256, to use an intensity component more shape representative. To compose the database of range images, the samples were also scanned with the 3D scanner we proposed in Orjuela et al, 13 in which the depth information of the surface of a carpet is digitize. To scan, the carpet is placed on a drum and on its surface a uniform line is project.…”

Section: Methodsmentioning

confidence: 99%

“…12,13,15 Particularly, using an extension of the known rotational Local Binary Pattern (LBPROT) 17 technique named LBPRMC (from Local Binary Pattern Rotational, Mirrored and Complemented invariant) has shown that grouping patterns increases the linear rank of the features related to the degree of wear as well as the discrimination between consecutive degrees of wear.…”

Section: Introductionmentioning

confidence: 99%

“…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%

See 2 more Smart Citations

Analysing wear in carpets by detecting varying local binary patterns

et al. 2011

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analysing wear in carpets by detecting varying local binary patterns

et al. 2011

Self Cite

View full text Add to dashboard Cite

show abstract

“…The range images were composed by scanning the carpet samples with the scanner based on structured light described in Orjuela et al [12]. The surface reconstruction of the range image was based on a wavelet edge detector [17].…”

Section: Methodsmentioning

confidence: 99%

“…Recently, we have proposed an automatic assessment system based on extracting texture parameters from intensity color and depth (range) images [11]. The range images are obtained using our own scanner specifically designed for carpets [12]. We have composed our own carpet database following the European standard [3,4].…”

Section: Introductionmentioning

confidence: 99%

Quantifying Appearance Retention in Carpets Using Geometrical Local Binary Patterns

Quiñones

Orjuela

Ortiz-Jaramillo

et al. 2011

Advanced Concepts for Intelligent Vision Systems

View full text Add to dashboard Cite

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.

show abstract

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

Vargas

Jaramillo²,

Meulemeester

et al. 2010

Advanced Concepts for Intelligent Vision Systems

View full text Add to dashboard Cite

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.

show abstract

Feature extraction of the wear label of carpets by using a novel 3D scanner

Cited by 9 publications

References 13 publications

Analysing wear in carpets by detecting varying local binary patterns

Analysing wear in carpets by detecting varying local binary patterns

Quantifying Appearance Retention in Carpets Using Geometrical Local Binary Patterns

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

Contact Info

Product

Resources

About