A statistical approach for textile fault detection

Abouelela, A.; Abbas, Irfan; deeb, I. El; Nassar, Salwa M.

doi:10.1109/icsmc.2000.884431

Cited by 9 publications

(5 citation statements)

References 3 publications

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“…It is worth mentioning that the zero-mean module shows better performance than the normalization module previously proposed in (Abouelela et al, 2000;Daul et al, 1998) for plain weave fabrics inspection. This is due to the fact that the proposed algorithm depends mainly on the variance as a measure of the local homogeneity in different parts of the inspected image.…”

Section: Step 1: Zero-mean Imagementioning

confidence: 99%

“…The proposed algorithm is based on a set of preprocessing and processing modules presented and illustrated in previous studies (Abouelela et al, 2000(Abouelela et al, , 2002. Various parameters and factors are adjusted and tuned to be suitable for inspection of textile fabrics dealt with in this study (plain weave fabrics).…”

Section: Detection Algorithmmentioning

confidence: 99%

“…The essence of the proposed automatic inspection system is attenuating the background texture and accentuating the defected parts (Abouelela et al, 2000). So, the smoothing module deals with the first problem of background texture attenuation.…”

Section: Step 2: Median Smoothed Imagementioning

confidence: 99%

See 2 more Smart Citations

Automated vision system for localizing structural defects in textile fabrics

Abouelela

Abbas

Eldeeb

et al. 2005

Pattern Recognition Letters

108

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Section: Step 1: Zero-mean Imagementioning

confidence: 99%

Section: Detection Algorithmmentioning

confidence: 99%

See 1 more Smart Citation

Automated vision system for localizing structural defects in textile fabrics

Abouelela

Abbas

Eldeeb

et al. 2005

Pattern Recognition Letters

108

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“…Imperfections in the fabric are accepted as if these are ''40 points per 100 yards''. 8,29,30 So, Figure 6(a) shows that fabrics which are received in the cutting-room contain variable ratios of imperfections, for example, stains, spots, contamination, dust, marks, holes, cuts, mis-ends, mis-picks, and so on. Usually, if flaws are observed, then these are removed by cutting fabric across the entire width during fabric spreading.…”

Section: Resultsmentioning

confidence: 99%

Reducing fabric wastage through image projected virtual marker (IPVM)

Naveed

Hussain

Zhong

2017

Textile Research Journal

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In apparel manufacturing, more than 50% of the total cost is consumed by the fabric in comparison to the rest of the materials. The efficient use of fabric may result in the reduction of considerable expenditure and efforts. Therefore, the purpose of this study is to propose an image projected virtual marker (IPVM) onto the surface of fabric layers/plies to improve fabric utilization and visual quality control. 20 garment production orders are scheduled for experimental work to investigate the utilization of fabrics with imperfections. The statistical analysis indicates a 3.5%–4% improvement in the fabric utilization, a 22% improvement in throughput time of visual inspection, and a 25% reduction of labor intensity during visual inspection in the cutting room. The result implies that the proposed method is an efficient approach to improve fabric utilization and cost reduction.

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“…For the analysis of fabric textures, several statistical approaches had been used and developed [16][17], such as some methods based on the Fourier analysis of the grey levels of images [18], on the Gabor filtering [19,20] and wavelets with adaptive bases [21]. In addition, an approach based on an image processing, developed for the study of liquid crystals [22], had been proposed by one of the authors of this article in [23][24][25].…”

Section: Introductionmentioning

confidence: 99%

The GIMP Retinex Filter Applied to the Fabric Fault Detection

Sparavigna¹,

Marazzato²

2017

ijSciences

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In this paper we are proposing the use of a Retinex filter, the GIMP Retinex, for improving the methods for fabric fault detection based on image processing. Since the Retinex filtering is simulating the human vision, it can act in the processing of the images as the trained staff of textile industry is acting in the visual inspection of fabrics on off-line stations. Here some examples are proposed. These examples show that an image preprocessing based on a Retinex filter can help any further analysis aimed to detect the presence of defects.

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A statistical approach for textile fault detection

Cited by 9 publications

References 3 publications

Automated vision system for localizing structural defects in textile fabrics

Automated vision system for localizing structural defects in textile fabrics

Reducing fabric wastage through image projected virtual marker (IPVM)

The GIMP Retinex Filter Applied to the Fabric Fault Detection

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