Shaped Fiber Identification Using a Distance-Based Skeletonization Algorithm

Wan, Yan; Yao, Li; Zeng, Peifeng; Xu, Bugao

doi:10.1177/0040517509348329

Cited by 11 publications

(6 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the other hand, ML and AutoML algorithms were also used in the textile industry: Wan et al (2009) introduced an automated identification system for fibre cross-sectional shapes using a nonlinear Support Vision Machine, a typical ML method. Ribeiro et al (2020a) compared three input feature representation strategies related to fabric design and finishing Identifying leather type and authenticity processes.…”

Section: Ijcst 361mentioning

confidence: 99%

Identifying leather type and authenticity by optical coherence tomography

Sabuncu,

Özdemir

2023

IJCST

View full text Add to dashboard Cite

PurposeThis study aims to identify leather type and authenticity through optical coherence tomography.Design/methodology/approachOptical coherence tomography images taken from genuine and faux leather samples were used to create an image dataset, and automated machine learning algorithms were also used to distinguish leather types.FindingsThe optical coherence tomography scan results in a different image based on leather type. This information was used to determine the leather type correctly by optical coherence tomography and automatic machine learning algorithms. Please note that this system also recognized whether the leather was genuine or synthetic. Hence, this demonstrates that optical coherence tomography and automatic machine learning can be used to distinguish leather type and determine whether it is genuine.Originality/valueFor the first time to the best of the authors' knowledge, spectral-domain optical coherence tomography and automated machine learning algorithms were applied to identify leather authenticity in a noncontact and non-invasive manner. Since this model runs online, it can readily be employed in automated quality monitoring systems in the leather industry. With recent technological progress, optical coherence tomography combined with automated machine learning algorithms will be used more frequently in automatic authentication and identification systems.

show abstract

Section: Ijcst 361mentioning

confidence: 99%

Identifying leather type and authenticity by optical coherence tomography

Sabuncu,

Özdemir

2023

IJCST

View full text Add to dashboard Cite

show abstract

“…In order to avoid overfitting and high-dimensionality problems, SVMs choose the maximum margin separating the hyperplane and defined a kernel function to map the training data into a higher-dimensional feature space. SVM has been applied to different textile problems, such as predicting fabric type 29 and fabric parameters, 30 predicting yarn properties, [31][32][33] fiber identification, 34 and color management 35 in textile printing and dyeing. Similar to the ANN, SVM has also been applied for quality management 36,37 in the textile sector, especially for defect classification 38 in textile textures.…”

Section: Support Vector Machine In Textile Industrymentioning

confidence: 99%

“…In addition to performing linear classification, SVMs can efficiently perform a nonlinear classification by mapping input space into a high-dimensional, even infinitedimensional, feature space. As a nonlinear SVM study example, Wan et al 34 introduced an automated identification system for fiber cross-sectional shapes. Firstly, the shape features were characterized using a distance-based Skeletonization algorithm, and then, SVM with a nonlinear kernel function was implemented to classify shaped fibers; a total of 1200 samples have been evaluated.…”

Section: Support Vector Machine In Textile Industrymentioning

confidence: 99%

Data mining and machine learning in textile industry

Yıldırım

Birant

Alpyıldız

2017

WIREs Data Min & Knowl

View full text Add to dashboard Cite

Data mining has been proven useful for knowledge discovery in many areas, ranging from marketing to medical and from banking to education. This study focuses on data mining and machine learning in textile industry as applying them to textile data is considered an emerging interdisciplinary research field. Thus, data mining studies, including classification and clustering techniques and machine learning algorithms, implemented in textile industry were presented and explained in detail in this study to provide an overview of how clustering and classification techniques can be applied in the textile industry to deal with different problems where traditional methods are not useful. This article clearly shows that a classification technique has higher interest than a clustering technique in the textile industry. It also shows that the most commonly applied classification methods are artificial neural networks and support vector machines, and they generally provide high accuracy rates in the textile applications. For the clustering task of data mining, a K-means algorithm was generally implemented in textile studies among the others that were investigated in this article. We conclude with some remarks on the strength of the data mining techniques for textile industry, ways to overcome certain challenges, and offer some possible further research directions.

show abstract

“…50 Because of the micrometer resolution images they provide, OCT scans can be used to precisely measure distances and fabric thicknesses and estimate gap widths. 51,52 On the other hand, ML and AutoML algorithms have also been used in the textile industry: Wan et al 53 introduced an automated identification system for fiber cross-sectional shapes using a nonlinear support vision machine, a typical ML method. Ribeiro et al 54 compared three input feature representation strategies related to fabric design and finishing processes.…”

mentioning

confidence: 99%

“…On the other hand, ML and AutoML algorithms have also been used in the textile industry: Wan et al. 53 introduced an automated identification system for fiber cross-sectional shapes using a nonlinear support vision machine, a typical ML method. Ribeiro et al.…”

mentioning

confidence: 99%

Optical coherence tomography imaging can identify Merino lambs’ wool using automatic machine learning vision

Sabuncu

Özdemi̇r

2023

Textile Research Journal

View full text Add to dashboard Cite

Merino lambs’ wool fiber has a unique chemical structure that gives the wool many unique properties and technical benefits. For example, the small fiber diameters mean that Merino wool is soft, countering the scratchiness commonly associated with wool. It is also biodegradable, organic, and environmentally friendly, making it a popular choice of sustainable fabric material. Unfortunately, some fiber sellers unduly sell coarse wool tops to wool yarn factories as Merino wool tops. It is, therefore, an important task to identify the actual wool type for quality assurance in the textile manufacturing process. This paper describes applying the spectral-domain optical coherence tomography (OCT) imaging and automatic machine learning (AutoML) techniques for distinguishing Merino wool from coarse wool. We present the results of wool measurements that were performed by the OCT scans and AutoML algorithms. We conclude that OCT imaging and AutoML algorithms can be applied to distinguish Merino wool from coarse wool in a simple, non-destructive, and contactless manner.

show abstract

Shaped Fiber Identification Using a Distance-Based Skeletonization Algorithm

Cited by 11 publications

References 14 publications

Identifying leather type and authenticity by optical coherence tomography

Identifying leather type and authenticity by optical coherence tomography

Data mining and machine learning in textile industry

Optical coherence tomography imaging can identify Merino lambs’ wool using automatic machine learning vision

Contact Info

Product

Resources

About