New Concepts of Yarn Hairiness

Barella, A.

doi:10.1080/19447015608665204

Cited by 21 publications

(18 citation statements)

References 4 publications

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“…The history of yarn hairiness measurements dates back to the 50's to the pioneering works of Barella [2] and Onions [36]. Since then various approaches to yarn hairiness determination have been proposed.…”

Section: Review Of Existing Approaches To Yarn Hairiness Measurementsmentioning

confidence: 99%

“…The microscopic image used to be projected on the screen or photographed. Then the number and length of protruding, looped and wild fibres per yarn unit length were measured manually [2,23,28,42]. Such measurements were laborious and time consuming.…”

Section: Review Of Existing Approaches To Yarn Hairiness Measurementsmentioning

confidence: 99%

“…However, it definitely depends on the fibres on the outer layer of the yarn that do not directly adhere to the core. Therefore, most commonly it is defined by means of some properties of protruding fibres, such as: number, length, shape (protruding ends or loops) etc., per unit of yarn core length [2,23].…”

Section: Introductionmentioning

confidence: 99%

“…Computer vision systems are commonly used in many branches of science, medicine and industry [43]. In textile, visual assessment is one of the fundamental methods of yarn [2,36] and final products [18] quality evaluation and also of yarn [7,26,55] and fabric [54] structure analysis. For more than 30 years computer vision techniques have been used in textile science for yarn quality inspection [3,27,49].…”

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%

Section: Review Of Existing Approaches To Yarn Hairiness Measurementsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Image processing and analysis algorithms for yarn hairiness determination

Fabijańska

Jackowska-Strumiłło

2012

Machine Vision and Applications

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show abstract

“…Another common method was direct measurement of yarn diameter manually, using magnified images obtained from a microscope or the projection of the shadow on a screen through some magnifying lenses. 6 However, this method was not favored due to its tedious nature. An interesting approach was adopted by van Issum and Chamberlain 7 where they photographed a running yarn in such a way that a considerable length of the specimen passed before the camera while the shutter was open.…”

Section: Introductionmentioning

confidence: 99%