A novel method for the identification of weave repeat through image processing

Ajallouian, F.; Tavanai, Hossein; Palhang, Maziar; Ravandi, Seyed Abdolkarim Hosseini; Sadri, Saeed; Matin, Khairul

doi:10.1080/00405000701660244

Cited by 28 publications

(27 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Other widely used spatial-domain methods utilize the co-occurrence matrix, 12 the quadratic local extremum, 13 and morphology analysis. 14 However, owing to the interstices between the yarns, the subsequently identified color of yarns may produce interference.…”

Section: Related Workmentioning

confidence: 99%

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

Meng

Wang

Pan

et al. 2020

Textile Research Journal

View full text Add to dashboard 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.

show abstract

Section: Related Workmentioning

confidence: 99%

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

Meng

Wang

Pan

et al. 2020

Textile Research Journal

View full text Add to dashboard Cite

show abstract

“…These existing methods were known to have some disadvantages. Put it in detail, [1][2][3] were usually unable to deal with the complicated patterns, [4][5][6] required the previous recognition of the weave/color pattern to be absolutely correct, while [7,8] needed an extra pattern database containing all the common patterns as the precondition for the repeat unit extraction.…”

Section: Introductionmentioning

confidence: 99%

A Novel Weave/Color Repeat Extraction Method with Error Tolerance

Zhou

Sheng

et al. 2014

Journal of Engineered Fibers and Fabrics

View full text Add to dashboard Cite

In this paper, we propose a simple but efficient method to identify the correct repeat unit in a yarn-dyed fabric weave pattern and color pattern automatically, especially when they contain error pattern recognitions. With only the color pattern of the fabric to be detected, our method can solve three problems simultaneously, even though there are errors in color pattern recognition. The method extracts the weave/color repeat unit, identifies the full weave pattern, and locates and rectifies the misrecognition in color pattern if there is one. The layout of color yarns is obtained by statistical solution. The weave pattern is partly determined by the correlation between color pattern and weave pattern, while that left can be identified by tentative grouping. In this process, the weave repeat unit is obtained when every group achieves internal uniformity; moreover the misrecognition in color pattern can be located and rectified simultaneously. Finally, the color repeat unit can also be extracted in the same way. Experiments on real fabrics validate the effectiveness of our method in extracting weave/color repeat unit from color pattern which contains errors.

show abstract

“…So they cannot obtain the weave patterns of the fabrics. Ajallouian et al (2009) used a new algorithm based on image processing techniques to identify weave repeat of woven fabric. They enhanced the fabric image with a steerable vertical filter and segmented it into blocks showing either a warp or a weft point.…”

Section: Introductionmentioning

confidence: 99%

Automatic recognition of woven fabric pattern based on image processing and BP neural network

Pan

Gao

Liu

et al. 2011

Journal of the Textile Institute

View full text Add to dashboard Cite

As there are error judgments of float type in the traditional method based on image processing, it is hard to determine the woven fabric pattern from the recognition results. To solve this problem, fuzzy C-means (FCM) algorithm was selected to classify the floats into two groups in the experiment, and BP neural network is chosen to recognize woven fabric pattern. White-black co-occurrence matrix is used to extract its texture features. The texture and structure features of the normal fabrics extracted from the classification are input into the neural network to complete the learning process. During the recognition process, the texture features of the fabric are extracted from the classification results with white-black co-occurrence matrix. The structure features are extracted simultaneously. These features are then input into BP neural network and woven fabric pattern would be output from the neural network. The experiment on actual fabrics proves that the method proposed in this study has fault tolerant ability, and it can recognize fabric patterns correctly.

show abstract

A novel method for the identification of weave repeat through image processing

Cited by 28 publications

References 13 publications

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

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

A Novel Weave/Color Repeat Extraction Method with Error Tolerance

Automatic recognition of woven fabric pattern based on image processing and BP neural network

Contact Info

Product

Resources

About