Measuring Reflectance Spectra of Textile Fabrics by Scanner

Shams‐Nateri, Ali

doi:10.4172/2165-8064.1000102

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…The sensitivity data bases consisted of a database from RIT [23] that contained 28 cameras and a database of 20 phone cameras [24]. The spectral databases include reflectivities of a textile database [25] (4827 samples), an X-Rite SG chart (140 samples), a Digieye DT chart (240 samples), and a set of radiant spectra measured in situ (2262 samples) [26]. We computed E values that resulted from the LMMSE method for each data set with each camera for a level of signal dependent noise that was given by a scale factor of 0.8 in each of the color channels.…”

Section: Comparison Of Measuresmentioning

confidence: 99%

Evaluation of Figures of Merit for Colorimetric Cameras

Vrhel¹,

Trussell²

2022

jist

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We investigate the relationship between goodness measures of spectral sensitivities to actual E performance in the presence of signal dependent noise. We show that the Vora value does not perform as well as Sharma's figure of merit (FOM). In addition, we show that Sharma's FOM has issues when the spectral samples include lower luminance data. We introduce an FOM that accounts for signal dependent noise and has a linear relationship to E performance. The improvement introduced by including signal dependent noise in the FOM results in closer relationships of the FOM to colorimetric accuracy in all cases but is especially important when the ensemble under investigation has a wide range of luminance values.

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Section: Comparison Of Measuresmentioning

confidence: 99%

Evaluation of Figures of Merit for Colorimetric Cameras

Vrhel¹,

Trussell²

2022

jist

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“…In addition, due to the advanced functionality and improved quality of scanners, they are now commonly available for the color measurement of objects in different textiles applications. [1][2][3][4][5][6][7] Two technologies used in flatbed scanners are Charge Coupled Device (CCD) and Contact Image Sensors (CIS). CCD-based scanners include an array of tiny sensors or photosensitive cells.…”

Section: Introductionmentioning

confidence: 99%

“…Interaction effects plot for A, variance of red channel; B, variance of green channel; and C, variance of blue channel the scanner RGB values were converted to CIE L*a*b* using the sRGB equation. The transformation from the RGB value of scanner to CIEXYZ can be carried out as follows3…”

mentioning

confidence: 99%

Optimizing the setup of a flatbed color scanner for color measurement using full factorial design

Hasanlou

Shams‐Nateri

Izadan

2019

Color Research & Application

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The application of flatbed color scanners for the measurement of color in various industries, especially textile industry, has received a great deal of attention. The initial setup of a scanner and the investigation of factors with a significant effect on scanner outputs are necessary to measure a reliable color using the scanner. In the present study, the setup of a flatbed color scanner was optimized using a full factorial design technique. Three factors, including the scanning position on the glass surface of the scanner (A), cutting size of an image in pixels (B), and bit depth (C), at different levels as input factors, and signal-to-noise ratio and variance of the scanned image as the responses were considered for the evaluation of the reliability of the scanner color measurement. The results of analyzing the factorial design indicate that each of the three factors played a significant role in the scanner setup for the color measurement so that factor A demonstrated the most significant impact on the responses. K E Y W O R D Scalibration, color, factorial design, optimization, scanner, signal-to-noise ratio

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Estimation of fabric opacity by scanner

2014

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Purpose – This study aimed to use a scanner as a low-cost method for measuring the opacity of textile fabric. Textile fabrics must have specific ranges of opacity according to their uses for shirting, curtaining, etc. In this way, opacity is an important property in the textile industry. Conventionally, textile opacity is estimated using a spectrophotometer, which is an expensive method. Design/methodology/approach – In this study a scanner was used as a low-cost method for measuring the opacity of textile fabric. The opacity was estimated by using red, green and blue (RGB) parameters of images of fabric against white and black background. Findings – The accuracy of opacity estimation was improved by converting RGB into several color spaces. The best opacity estimation was obtained by using the XYZ color space. In addition, using a regression method, the best estimation was obtained by using a fourth-order polynomial regression with the LSLM color space. Originality/value – The opacity of fabric has been measured by spectrophotometer, but in this study, the opacity of fabric was measured by scanner as a low cost device and also with novel and simple method. This method achieved acceptable accuracy for opacity estimation. The obtained result is comparable with spectrophotometer results.

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Measuring Reflectance Spectra of Textile Fabrics by Scanner

Cited by 4 publications

References 17 publications

Evaluation of Figures of Merit for Colorimetric Cameras

Evaluation of Figures of Merit for Colorimetric Cameras

Optimizing the setup of a flatbed color scanner for color measurement using full factorial design

Estimation of fabric opacity by scanner

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