Automatic detection of layout of color yarns of yarn‐dyed fabric. Part 3: Double‐system‐Mélange color fabrics

Textile Research Journal

Pan

et al. 2020

Self Cite

The layout of colored yarns in yarn-dyed fabrics is a significant part of designing and production in the textile industry, which is still analyzed manually at present. Existing methods based on image processing have some limitations in accuracy and stability. Therefore, an automatic method is proposed to recognize the layout of colored yarns and some other basic fabric structure parameters: the fabric density and weave pattern. First, a large dataset with fabric structure parameters is constructed. The fabric images are captured by a wireless portable device. Then the yarns and floats are accurately located using a novel multi-task and multi-scale convolutional neural network. Finally, a density-based color clustering algorithm is proposed to recognize the layout of colored yarns. The results of extensive experiments show that the proposed method can automatically identify the basic structure parameters with high effectiveness and robustness.

Section: Related Workmentioning

confidence: 99%

Recognition of the layout of colored yarns in yarn-dyed fabrics

Meng

Textile Research Journal

Pan

et al. 2020

Self Cite

“…Image enhancement methods: Gaussian filter; 2,3 top-hat transform and bottom-hat transform; 4 coherence-enhancing diffusion; 5 2. Fabric skewness correction methods: Hough transform, 2,3,[6][7][8][9] and Radon transform; 5 3. Separate single yarn methods: Fourier transform, 10 wavelet transform, 5 gray projection method, [2][3][4][6][7][8]10 and mathematical statistics method 9 1.…”

Section: Separate Single Yarn From Fabric Imagementioning

confidence: 99%

“…Group I-separate single yarn from fabric image. [2][3][4][5][6][7][8][9][10] In this group, the single yarn was segmented from the fabric image first. Second, the clustering methods were applied for recognizing the color pattern of yarn-dyed fabric image.…”

Section: Introductionmentioning

confidence: 99%

Intelligent recognition of the patterns of yarn-dyed fabric based on LSRT images

Meng

Journal of Engineered Fibers and Fabrics

et al. 2019

Self Cite

In this article, an intelligent inspection method based on image analysis is proposed to identify the color and woven pattern of yarn-dyed fabric automatically. The local sequence images under the reflected light and transmitted light (LSRT images), which consist of reflection sequence images and transmission sequence images, are first captured by a fabric image acquisition device. Then the Fourier transform, image segmentation, and arithmetic operations are employed to the transmission sequence images to determine the location of weave points. Subsequently, the L*a*b* values of each weave point are extracted from the reflection sequence images. To inspect the color pattern, X-means clustering algorithm is used to classify the weave points based on the L*a*b* values. To detect the woven pattern, incomplete weave pattern matrixes of all sequence images are used to match the weave pattern database. Eight LSRT images of each yarn-dyed fabric sample are tested by the proposed method. The experimental results proved that the proposed method can recognize the color and weave pattern of yarn-dyed fabric with satisfactory accuracy and good robustness.

“…11,12 Li et al 13 used x-means clustering algorithm, which can accurately identify the color and texture of dyed fabrics, and had good robustness. Zhang et al [14][15][16] used FCM algorithm to recognize the color pattern and color yarn layout for yarn-dyed fabrics. Pan et al 17 realized the automatic recognition of fabric texture structure by inputting BP neural network into the cluster segmentation results.…”

Section: Introductionmentioning

confidence: 99%

Automatic measurement of traditional Chinese costume from its silhouette through Fuzzy c-means clustering method

Zhang

Journal of Engineered Fibers and Fabrics

Xing

et al. 2020

As the precious cultural heritage of the Chinese nation, traditional costumes are in urgent need of scientific research and protection. In particular, there are scanty studies on costume silhouettes, due to the reasons of the need for cultural relic protection, and the strong subjectivity of manual measurement, which limit the accuracy of quantitative research. This paper presents an automatic measurement method for traditional Chinese costume dimensions based on fuzzy C-means clustering and silhouette feature point location. The method is consisted of six steps: (1) costume image acquisition; (2) costume image preprocessing; (3) color space transformation; (4) object clustering segmentation; (5) costume silhouette feature point location; and (6) costume measurement. First, the relative total variation model was used to obtain the environmental robustness and costume color adaptability. Second, the FCM clustering algorithm was used to implement image segmentation to extract the outer silhouette of the costume. Finally, automatic measurement of costume silhouette was achieved by locating its feature points. The experimental results demonstrated that the proposed method could effectively segment the outer silhouette of a costume image and locate the feature points of the silhouette. The measurement accuracy could meet the requirements of industrial application, thus providing the dual value of costume culture research and industrial application.