Color separation for colored fiber blends based on the fuzzy C‐means cluster

Lu, Yuzheng; Gao, Weidong; Liu, Jihong

doi:10.1002/col.20662

Cited by 3 publications

(6 citation statements)

References 72 publications

(84 reference statements)

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“…(c) are selected to represent the corresponding warp yarn. The specific location of the warp yarn center‐line is confirmed, as the following formula:

P_{i} = f i x ((L_{i} + L_{i + 1}) / 2)

where P i is the i th warp yarn center‐line. Step 2: The pixels in each warp yarn are classified into two colors by FCM algorithm in CIELAB color model separately . As the fabric image cannot be converted directly from the RGB color model to the CIELAB color model, the image is first changed to an XYZ color model as follows: For r is in [0, 1], if r > 0.04045,

r = {(\frac{r + 0.055}{1.055})}^{2.4}

, else

r = \frac{r}{12.92}

.…”

Section: Methodsmentioning

confidence: 99%

“…The FCM algorithm is an unsupervised clustering method and it attempts to minimize the objection function by organizing the data into different clusters . The objective function is as follows:

J_{m} (U, V) = \sum_{j = 1}^{n} \sum_{i = 1}^{c} u_{i, j}^{m} d_{_{i, j}}^{2}

where n is the data number; c is the cluster number; U is the membership degree matrix; V is the cluster center matrix; u i, j expresses the membership degree of the data point x j belonging to the i th group and satisfies the following two conditions: u i, j

\in

[0, 1] and

\sum_{i = 1}^{c} u_{i, j}^{}

=1, where m

\in

(1, +∞), is a weighting exponent that influences the fuzziness of the clusters and controls the sharing degree between different cluster groups.…”

Section: Methodsmentioning

confidence: 99%

“…Step 2: The pixels in each warp yarn are classified into two colors by FCM algorithm 24,25 in CIELAB color model separately. 18,[26][27][28] As the fabric image cannot be converted directly from the RGB color model to the CIELAB color model, the image is first changed to an XYZ color model as follows 18 The FCM algorithm is an unsupervised clustering method and it attempts to minimize the objection function by organizing the data into different clusters.…”

Section: Warp Color Recognitionmentioning

confidence: 99%

“…18,[26][27][28] As the fabric image cannot be converted directly from the RGB color model to the CIELAB color model, the image is first changed to an XYZ color model as follows 18 The FCM algorithm is an unsupervised clustering method and it attempts to minimize the objection function by organizing the data into different clusters. 24,25 The objective function is as follows:…”

Section: Warp Color Recognitionmentioning

confidence: 99%

See 3 more Smart Citations

Automatic detection of layout of color yarns of yarn‐dyed fabric. Part 1: Single‐system‐mélange color fabrics

Zhang

Pan

Gao

et al. 2014

Color Research & Application

Self Cite

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To recognize the layout of color yarns of single-system-m elange color fabric automatically, a novel FCM-based stepwise classification method is proposed in this article. This method consists of three main steps: (1) warp yarn segmentation, (2) weft color recognition, and (3) the layout of color warps recognition. In the first step, the yarn segmentation method based on mathematical statistics of subimages is adopted to localize warp yarns preliminarily; and then the segmentation results of warp yarn are corrected by misrecognized-boundary remove and missing-boundary interpolation. In the second step, the weft color is extracted based on RGB color histograms of whole fabric image. In the third step, the pixels in each warp yarn are classified into two clusters by fuzzy C-means clustering (FCM) algorithm in CIELAB color model separately, and the preliminary recognized layout of color warps is obtained. All warp colors are clustered by FCM algorithm in CIELAB color model again and the precise layout of color warps is output. The experimental and theoretical analysis proved that the proposed method can recognize the layout of color yarns of single-system-m elange color fabrics with satisfactory accuracy and good robustness.

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“…(c) are selected to represent the corresponding warp yarn. The specific location of the warp yarn center‐line is confirmed, as the following formula:

P_{i} = f i x ((L_{i} + L_{i + 1}) / 2)

r = {(\frac{r + 0.055}{1.055})}^{2.4}

, else

r = \frac{r}{12.92}

.…”

Section: Methodsmentioning

confidence: 99%

“…The FCM algorithm is an unsupervised clustering method and it attempts to minimize the objection function by organizing the data into different clusters . The objective function is as follows:

J_{m} (U, V) = \sum_{j = 1}^{n} \sum_{i = 1}^{c} u_{i, j}^{m} d_{_{i, j}}^{2}

\in

[0, 1] and

\sum_{i = 1}^{c} u_{i, j}^{}

=1, where m

\in

(1, +∞), is a weighting exponent that influences the fuzziness of the clusters and controls the sharing degree between different cluster groups.…”

Section: Methodsmentioning

confidence: 99%

Section: Warp Color Recognitionmentioning

confidence: 99%

Section: Warp Color Recognitionmentioning

confidence: 99%

See 2 more Smart Citations

Automatic detection of layout of color yarns of yarn‐dyed fabric. Part 1: Single‐system‐mélange color fabrics

Zhang

Pan

Gao

et al. 2014

Color Research & Application

Self Cite

View full text Add to dashboard Cite

show abstract

“…HSV stands for hue, saturation, and value, respectively. Note that hue H [ [08, 3608], saturation S [ [0.0, 1.0], and value V [ [0.0, 1.0] (Lu et al, 2011). HSV model is the most common cylindrical-coordinate representations of points in an RGB color model, which rearrange the geometry of RGB in an attempt to be more intuitive and perceptually relevant than the cartesian (cube) representation (Chen and Wu, 2005;Chan et al, 2001).…”

Section: Hsv Modelmentioning

confidence: 99%

Development of image pattern for textile based on FFT

Jiang

Wang

Liu

et al. 2012

International Journal of Clothing Science and Technology

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Purpose -The purpose of this paper is to research an auto generation method of developing FFT image and image pattern for textile based on FFT theory. Design/methodology/approach -In the research, a program was developed to generate FFT images using the FFT algorithm. The process of auto generation FFT image can be divided into the following steps: initializing the size of image, painting source image, giving the color pattern, transforming FFT image by FFT, designing mask template, and image pattern combining by point diagram. These image patterns can be used to apply on the textile. Findings -The results showed that the FFT images can be used for textile designer directly. The FFT images can also be used as elements for textile image design such as clothing. The auto generation FFT images by FFT reflect different modern sense of beauty from traditional geometric images. Research limitations/implications -There are many parameters that affect the art effect of FFT image generating by FFT algorithm. However, there is no discussion about the relationship between the parameters and art effect. Three dimension effects are not obvious in the simulation results by virtual clothing software. Originality/value -The paper presents a fundamental understanding of the property of the FFT image generating by FFT algorithm and application method of the image pattern in clothing.

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A novel 3D convolutional neural network model with supervised spectral regression for recognition of hyperspectral images of colored wool fiber

Qiu

Chen

Shen

et al. 2022

Color Research & Application

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Prior knowledge of textile fiber colors in blends is useful for color recipe assessment. There have been various methods to improve the accuracy of color recognition from the dataset of colored textile fibers in recent years. However, numerical assessments based on spectral feature and color difference is insufficient, in which the accuracy of color recognition can be affected by morphology and the uneven coloration on a single fiber. This paper proposes a novel 3D convolutional neural network model (3D‐CNN) with supervised spectral regression for the color recognition of hyperspectral images (HSI) of colored textile fiber. The proposed method obtained spatial‐spectral features based on 3D‐CNN, and the true spectrum of each class was used for supervised spectral regression to improve the accuracy. The loss function used was the sum of the supervised classification loss function and the spectral regression loss function model are optimized by mini‐batch‐based backpropagation. The proposed method was trained and tested on the HSI dataset composed of 100 colors of wool fibers acquired through a microscopic hyperspectral imaging system at a ×3.375 optical magnification. The experimental results showed that the proposed method exhibited better performance compared to numerical assessments and other deep learning models, except for efficiency. Specifically, it achieved better recognition performance on sub‐datasets of similar colored and light‐colored wool fiber where subtle inter‐class and large intra‐class variance existed.

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Color separation for colored fiber blends based on the fuzzy C‐means cluster

Cited by 3 publications

References 72 publications

Automatic detection of layout of color yarns of yarn‐dyed fabric. Part 1: Single‐system‐mélange color fabrics

Automatic detection of layout of color yarns of yarn‐dyed fabric. Part 1: Single‐system‐mélange color fabrics

Development of image pattern for textile based on FFT

A novel 3D convolutional neural network model with supervised spectral regression for recognition of hyperspectral images of colored wool fiber

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