Cotton Color Classification by Fuzzy Logic

Xu, Bugao; Dale, David; Huang, Yun; Watson, Michael D.

doi:10.1177/004051750207200607

Cited by 13 publications

(8 citation statements)

References 2 publications

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“…There have been a variety of studies conducted on the measurement of the color grade of cotton. 3–8 Past research has studied the relationship between color grade as reported by cotton classifiers and the color properties determined by the early disc colorimeters during the development of cotton color standards. 3 A major focus of previous studies has been on the development of methods or algorithms for improving the measurement of the overall color grade of a cotton sample.…”

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confidence: 99%

“…3 A major focus of previous studies has been on the development of methods or algorithms for improving the measurement of the overall color grade of a cotton sample. These studies have included the use of modern techniques such as fuzzy logic to enhance analysis of the color information, 4 as well as applying artificial neural networks to evaluate the impact of impurities such as trash particles. 5 In recent years efforts have been made to use CIE color parameters or to use color spectrophotometers to develop traceable color standards for cotton grading.…”

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confidence: 99%

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An investigation into the intra-sample variation in the color of cotton using image analysis

Cui¹,

Cai

Rodgers³

et al. 2013

Textile Research Journal

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The colorimeter method is widely used to identify the color and grade of a cotton sample, but this method does not give information about the variation of color within the sample. We have conducted an investigation using an image analysis method to study the intra-sample distribution and variation in cotton color. High-resolution color images of cotton samples were obtained using a color scanner. For each image the Rd and + b values of the image and specific sub-areas within it were computed to analyze the color distribution. Intra- and inter-sample variations in Rd and + b values and color grade were compared with those obtained by the HVI system. The results show that for the same cotton substrate, although the variations among the color grades of different samples (replicates) may be very small, intra-sample color variations are evident, the colors of the sub-areas being distributed over a range of areas of the cotton color chart. In other words, cotton samples of the same overall color grade may exhibit different intra-sample color variations and distribution. A description of the intra-sample color variations and distributions obtainable using an image analysis method may allow a more comprehensive evaluation of color characteristics on cotton.

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confidence: 99%

mentioning

confidence: 99%

An investigation into the intra-sample variation in the color of cotton using image analysis

Cui¹,

Cai

Rodgers³

et al. 2013

Textile Research Journal

View full text Add to dashboard Cite

show abstract

“…Finally, the center of gravity 13,14 is used as a defuzzifier to convert the output of fuzzy inference, μC0, into a clear output value y 0 where μC0(yi) is the membership value of y i , which belongs to the fuzzy set C0, and y0 is the MPCI value.…”

Section: Methodsmentioning

confidence: 99%

Optimum processing parameters of sueding fabric comfort by applying the Taguchi method and fuzzy theory

Kuo

Lin

2019

Textile Research Journal

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In recent years, as consumers have increasingly higher demands for the functionality of clothing, fabrics with additional functionality have been developed. The state of physical functionality experienced by a wearer under a given environmental condition is greatly influenced by the tactile, thermal and moisture transport properties of the fabric. In modern textiles, finishing is generally used to improve the appearance or other properties of fabrics. Sueding is a common finishing method. In order to meet the requirements of functionality in the process of sueding, the production process must be adjusted according to the features of the fabrics. The main method is the setting of processing parameters. This study focuses on the water vapor transmission and thermal transport, which are the main functional characteristics, and uses the fuzzy approach to acquire the combination of sueding processing parameters to optimize multiple functional characteristics. Firstly, for the main processing parameters of the sueding machine, the Taguchi method is used to obtain an orthogonal array for the experimental design. The measured water vapor transmission and thermal transport data of the fabric are used to calculate the signal-to-noise ratio. An analysis of variance is conducted to obtain the significant factors with a greater impact on functional characteristics. The fuzzy approach is used to analyze and obtain the optimal combination of processing parameters of multiple functional characteristics. This optimal combination of processing parameters is used to set the processing parameters. The reliability and reproducibility of the experiment are verified by confirmation experiments.

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“…Since the early work of Thomasson (1993), recent studies have attempted to move toward image-based color measurement so that trash particles can be excluded from the color measurement (Lieberman and Siddaiah, 1999;Xu et al, 1997Xu et al, , 2001Xu et al, , and 2002. These attempts have involved commercially available color cameras and scanners adapted to the purpose of cotton color measurement.…”

Section: Literature Reviewmentioning

confidence: 99%

Image-Processing Solution to Cotton Color Measurement Problems: Part I. Instrument Design and Construction

Thomasson¹,

Shearer²,

Byler³

2005

Transactions of the ASAE

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Trash particles enmeshed in bulk cotton fiber samples interfere with conventional cotton color measurement accuracy. A new instrument was designed and constructed for reducing trash effects on cotton color measurement, while maintaining the traditional system of cotton color measurement. The instrument's illumination system included four quartz−tungsten−halogen lamps in aluminum elliptical reflectors. The instrument's sensor was a panchromatic video camera that acquired images through optical color filters on a rotating wheel. The sensitivities of the various measurements were rigorously considered to maximize the dynamic range over which each measurement was made. The system's camera was connected to a computer through a frame grabber. Software was written to control the filter wheel, image acquisition, color/trash computations, and data recording. Image processing was employed to differentiate trash particles from cotton in the images. Color was calculated from the image portion judged by image analysis to be cotton. The system was tested on a large number of cotton samples and performed as designed and without any software or hardware failures.

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Cotton Color Classification by Fuzzy Logic

Cited by 13 publications

References 2 publications

An investigation into the intra-sample variation in the color of cotton using image analysis

An investigation into the intra-sample variation in the color of cotton using image analysis

Optimum processing parameters of sueding fabric comfort by applying the Taguchi method and fuzzy theory

Image-Processing Solution to Cotton Color Measurement Problems: Part I. Instrument Design and Construction

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