Novel Colour Clustering Method for Interlaced Multi-colored Dyed Yarn Woven Fabrics

Zhang, Jie; Xin, Binjie; Shen, Cuicui; Fang, Heng; Cao, Yanjuan

doi:10.5604/12303666.1152535

Cited by 9 publications

(7 citation statements)

References 15 publications

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“…4 Group II-not separate single yarn. 1,[11][12][13][14][15][16][17][18] In this group, the yarn-dyed fabric images were detected directly without segmenting out the yarn. The whole images were preprocessed first based on some image processing methods.…”

Section: Introductionmentioning

confidence: 99%

“…The whole images were preprocessed first based on some image processing methods. Then genetic algorithm, 15 backpropagation (BP) neural network, 11 probabilistic model and hierarchical segmentation, 12 logical analysis, 1 and cluster method 13,14,[16][17][18] were utilized to inspect the color pattern of the whole yarn-dyed fabric image. The weave pattern could not be detected using this group of methods.…”

Section: Introductionmentioning

confidence: 99%

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Intelligent recognition of the patterns of yarn-dyed fabric based on LSRT images

Meng

Wang

et al. 2019

Journal of Engineered Fibers and Fabrics

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In this article, an intelligent inspection method based on image analysis is proposed to identify the color and woven pattern of yarn-dyed fabric automatically. The local sequence images under the reflected light and transmitted light (LSRT images), which consist of reflection sequence images and transmission sequence images, are first captured by a fabric image acquisition device. Then the Fourier transform, image segmentation, and arithmetic operations are employed to the transmission sequence images to determine the location of weave points. Subsequently, the L*a*b* values of each weave point are extracted from the reflection sequence images. To inspect the color pattern, X-means clustering algorithm is used to classify the weave points based on the L*a*b* values. To detect the woven pattern, incomplete weave pattern matrixes of all sequence images are used to match the weave pattern database. Eight LSRT images of each yarn-dyed fabric sample are tested by the proposed method. The experimental results proved that the proposed method can recognize the color and weave pattern of yarn-dyed fabric with satisfactory accuracy and good robustness.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Intelligent recognition of the patterns of yarn-dyed fabric based on LSRT images

Meng

Wang

et al. 2019

Journal of Engineered Fibers and Fabrics

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“…60,61 K-means is an easily implemented algorithm that divides the given dataset into k clusters by determining the centroids of each cluster. Zhang et al 54 used this algorithm to cluster interlaced, multicolored, dyed, yarn-woven fabrics. Images captured from fabrics were divided into three subimages in red, blue and green and were then filtered in Lab color space; finally, the algorithm was processed for color clustering.…”

Section: Clustering In Textile Industrymentioning

confidence: 99%

“…It helps us better understand the characteristics of data because fewer groups are more easily interpreted. Several studies have focused on the application of the clustering process in the textile industry with the aid of algorithms named K-means, [54][55][56][57][58] Fuzzy C-means, 59 and Hierarchical. 60,61 K-means is an easily implemented algorithm that divides the given dataset into k clusters by determining the centroids of each cluster.…”

Section: Clustering In Textile Industrymentioning

confidence: 99%

Data mining and machine learning in textile industry

Yıldırım

Birant

Alpyıldız

2017

WIREs Data Min & Knowl

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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.

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“…In addition to classification studies, several studies implemented clustering techniques on fabric data, especially k-means [23], fuzzy c-means [24], and hierarchical [25] algorithms. In these studies, fabric images were first preprocessed and parameters were defined by a feature extractor, and then a clustering algorithm was applied.…”

Section: Related Workmentioning

confidence: 99%

Discovering the relationships between yarn and fabric properties using association rule mining

Yıldırım¹,

Birant²,

Alpyıldız³

2017

Turk J Elec Eng & Comp Sci

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Abstract:Investigation of the effects of yarn parameters on fabric quality and finding important parameters to achieve desired fabric properties are important issues for the design process with the aim to meet the needs of the textile industry and the consumer for complex and specific requirements of functionality. Despite many statistical and mathematical studies that predict and reveal specific properties of utilized yarn and fabric materials, a number of challenges continue to exist when evaluated in many perspectives, such as discovering complex relationships among material properties in data. Data mining plays an important role in discovering hidden patterns from fabric data and transforming it into knowledge. Therefore, the aim of the study is to uncover relationships between yarn parameters and fabric properties using an extended FP-Growth algorithm in association rule mining. This study extracts different types of frequent itemsets (closed, maximal, top-k, top-k closed, top-k maximal) that have not been determined in textile sector before.This article also proposes two novel concepts, closed frequent item and maximal frequent item, to identify significant items in data. In the experimental studies, the algorithm was executed on a real-world textile dataset with different support threshold values to compare the different types of patterns. Experimental results show that proposed approach is very useful for discovering rules related to yarn and fabric properties.

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Novel Colour Clustering Method for Interlaced Multi-colored Dyed Yarn Woven Fabrics

Cited by 9 publications

References 15 publications

Intelligent recognition of the patterns of yarn-dyed fabric based on LSRT images

Intelligent recognition of the patterns of yarn-dyed fabric based on LSRT images

Data mining and machine learning in textile industry

Discovering the relationships between yarn and fabric properties using association rule mining

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