Digital image processing and illumination techniques for yarn characterization

Ozkaya, Yasar A.; Acar, Memiş; Jackson, Michael R.

doi:10.1117/1.1902743

Cited by 28 publications

(32 citation statements)

References 21 publications

(21 reference statements)

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“…A few solutions using image processing and analysis algorithms for hairiness assessment have already been reported [15,17,22,32,33,38,39]. However, there is no commercial vision based system for textile yarn hairiness determination available on the market.…”

Section: Review Of Existing Approaches To Yarn Hairiness Measurementsmentioning

confidence: 99%

“…An interesting threshold based approaches for yarn core extraction was proposed by Ozkaya et al in [38]. The authors differentiate back-lit images and dark-field images.…”

Section: Yarn Core Segmentationmentioning

confidence: 99%

“…It that case the method can not be unambiguously adapted to the regarded images as it produces similar thresholds for both: the core and the yarn. The method for dark-field images proposed in [38] estimates threshold for yarn simply based on background intensities where no yarn is present. For core extraction a given number of rows is integrated along every column in order to find the core edges from the peaks in obtained intensity profiles.…”

Section: Yarn Core Segmentationmentioning

confidence: 99%

“…For more than 30 years computer vision techniques have been used in textile science for yarn quality inspection [3,27,49]. In modern computer vision systems image processing and analysis algorithms are used for an automatic measurement of important yarn quality parameters, such as hairiness [12,15,20,22,39,40], diameter [13,38], twist [17,41], thickness [17,49], faults [14], density and bulkiness [15], surface defects [29], etc.…”

Section: Introductionmentioning

confidence: 99%

“…Several image-based approaches dedicated to yarn properties assessment have already been proposed. However, these methods use various kinds of thresholding approaches for yarn core and protruding fibres segmentation [7,[12][13][14][15][16][17][18][19][20]22,27,29,[31][32][33]35,[38][39][40][41]52,54], which often lack universality as they either are dedicated to the certain class of images provided by the certain vision system or require certain yarn orientation and experimental setting of parameters by trial and error examination. This paper presents application of graph based method for yarn core extraction and high pass filtering based method for yarn segmentation.…”

Section: Introductionmentioning

confidence: 99%

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Image processing and analysis algorithms for yarn hairiness determination

Fabijańska

Jackowska-Strumiłło

2012

Machine Vision and Applications

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Yarn hairiness is one of the key parameters influencing fabric quality. In this paper image processing and analysis algorithms developed for an automatic determination of yarn hairiness are presented. The main steps of the proposed algorithms are as follows: image preprocessing, yarn core extraction using graph cut method, yarn segmentation using high pass filtering based method and fibres extraction. The developed image analysis algorithms quantify yarn hairiness by means of the two proposed measures such as hair area index and hair length index, which are compared to the USTER hairiness index-the popular hairiness measure, used nowadays in textile science, laboratories and industry. The detailed description of the proposed approach is given. The developed method is verified experimentally for two distinctly different yarns, produced by the use of different spinning methods, different fibres types and characterized by totally different hairiness. The proposed algorithms are compared with computer methods previously used for yarn properties assessment. Statistical parameters of the hair length index (mean absolute deviation, standard deviation and coefficient of variation) are calculated. Finally, the obtained results are analyzed and discussed. The proposed approach of yarn hairiness measurement is universal and the presented algorithms can be successfully applied in different vision systems for yarn quantitative analysis.

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Section: Review Of Existing Approaches To Yarn Hairiness Measurementsmentioning

confidence: 99%

“…An interesting threshold based approaches for yarn core extraction was proposed by Ozkaya et al in [38]. The authors differentiate back-lit images and dark-field images.…”

Section: Yarn Core Segmentationmentioning

confidence: 99%

Section: Yarn Core Segmentationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Image processing and analysis algorithms for yarn hairiness determination

Fabijańska

Jackowska-Strumiłło

2012

Machine Vision and Applications

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An algorithm for cross-fiber separation in yarn hairiness image processing

Deng,

Yu,

Wang

et al. 2023

Vis Comput

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Yarn hairiness affects yarn and fabric quality. The existing hairiness detection methods cannot discriminate crossover fibers or hairs. In order to accurately separate and detect crossover fibers, an algorithm of separating crossover hairs is proposed. By obtaining refined hair skeletons after image pretreatment, the positions of fiber intersection point were determined, and a hair information table according to the characteristics of the hair cross-point was constructed. After classifying each hair branch skeleton and screening out the hair common skeleton, the branch hair matching table by using the two end points of the true common hair skeleton adjacent to the hair branch was constructed. Through pairing the same cross-hair branch with the principle of the closest slope at the adjacent cross-end, each complete cross-hair skeleton was defined for hair count in a field of view. The detection results show that compared with the existing photoelectric hairiness detection instrument, the algorithm can realize the crossover hair separation, and calculate the length of complete crossover hairs and curved hairs with high accuracy. On average, the developed algorithm measures hair length about 11.1% longer than the manually measured results, while commercial apparatus would report hair length 62.5% shorter than the actual hair length.

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