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| rdf:type
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| rdfs:seeAlso
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| Description
| - Analysis of textile materials often includes measurement of structural anisotropy or directional orientation of textile object systems. To that purpose, the real-world objects are replaced by their images, which are analyzed, and the results of this analysis are used for decisions about the product(s). Study of the image data allows one to understand the image contents and to perform quantitative and qualitative description of objects of interest. This paper deals in particular with the problem of estimating the main orientation of fiber systems. Firstly, we present a concise survey of the methods suitable for estimating orientation of fiber systems stemming from the image analysis. The methods we consider are based on the two-dimensional discrete Fourier transform combined with the method of moments. Secondly, we suggest abandoning the currently used global, that is, all-at-once, analysis of the whole image, which typically leads to just one estimate of the characteristic of interest, and advise replacing it with a “local analysis”. This means splitting the image into many small, non-overlapping pieces, and estimating the characteristic of interest for each piece separately and independently of the others. As a result we obtain many estimates of the characteristic of interest, one for each sub-window of the original image, and – instead of averaging them to get just one value – we suggest analyzing the distribution of the estimates obtained for the respective sub-images. The proposed approach seems especially appealing when analyzing nonwoven textiles and nanofibrous layers, which may often exhibit quite a large anisotropy of the characteristic of interest.
- Analysis of textile materials often includes measurement of structural anisotropy or directional orientation of textile object systems. To that purpose, the real-world objects are replaced by their images, which are analyzed, and the results of this analysis are used for decisions about the product(s). Study of the image data allows one to understand the image contents and to perform quantitative and qualitative description of objects of interest. This paper deals in particular with the problem of estimating the main orientation of fiber systems. Firstly, we present a concise survey of the methods suitable for estimating orientation of fiber systems stemming from the image analysis. The methods we consider are based on the two-dimensional discrete Fourier transform combined with the method of moments. Secondly, we suggest abandoning the currently used global, that is, all-at-once, analysis of the whole image, which typically leads to just one estimate of the characteristic of interest, and advise replacing it with a “local analysis”. This means splitting the image into many small, non-overlapping pieces, and estimating the characteristic of interest for each piece separately and independently of the others. As a result we obtain many estimates of the characteristic of interest, one for each sub-window of the original image, and – instead of averaging them to get just one value – we suggest analyzing the distribution of the estimates obtained for the respective sub-images. The proposed approach seems especially appealing when analyzing nonwoven textiles and nanofibrous layers, which may often exhibit quite a large anisotropy of the characteristic of interest. (en)
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| Title
| - Estimation of Fiber System Orientation for Nonwoven and Nanofibrous Layers: Local Approach Based on Image Analysis
- Estimation of Fiber System Orientation for Nonwoven and Nanofibrous Layers: Local Approach Based on Image Analysis (en)
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| skos:prefLabel
| - Estimation of Fiber System Orientation for Nonwoven and Nanofibrous Layers: Local Approach Based on Image Analysis
- Estimation of Fiber System Orientation for Nonwoven and Nanofibrous Layers: Local Approach Based on Image Analysis (en)
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| skos:notation
| - RIV/46747885:24620/14:#0000463!RIV15-MSM-24620___
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| http://linked.open...avai/riv/aktivita
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| http://linked.open...avai/riv/aktivity
| - I, P(GA201/09/0755), P(LO1201), P(TA03010609)
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| http://linked.open...iv/cisloPeriodika
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| http://linked.open...vai/riv/dodaniDat
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| http://linked.open...aciTvurceVysledku
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| http://linked.open.../riv/druhVysledku
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| http://linked.open...iv/duvernostUdaju
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| http://linked.open...titaPredkladatele
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| http://linked.open...dnocenehoVysledku
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| http://linked.open...ai/riv/idVysledku
| - RIV/46747885:24620/14:#0000463
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| http://linked.open...riv/jazykVysledku
| |
| http://linked.open.../riv/klicovaSlova
| - Fiber system; digital image; Fourier analysis; covariance matrix analysis; moments of image; nanofibers layers; histogram; kernel density estimator (en)
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| http://linked.open.../riv/klicoveSlovo
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| http://linked.open...odStatuVydavatele
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| http://linked.open...ontrolniKodProRIV
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| http://linked.open...i/riv/nazevZdroje
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| http://linked.open...in/vavai/riv/obor
| |
| http://linked.open...ichTvurcuVysledku
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| http://linked.open...cetTvurcuVysledku
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| http://linked.open...vavai/riv/projekt
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| http://linked.open...UplatneniVysledku
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| http://linked.open...v/svazekPeriodika
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| http://linked.open...iv/tvurceVysledku
| - Chvojka, Jiří
- Kula, Jiří
- Tunák, Maroš
- Antoch, Jaromír
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| issn
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| number of pages
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| http://localhost/t...ganizacniJednotka
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