关键词: 组织点分割, 自动识别, K均值聚类, 模糊C均值算法, 高斯混合模型

Abstract: The structural parameters of woven fabrics mainly include warp and weft densities and fabric weave. The identification and analysis of these structural parameters is an important prerequisite for textile enterprises to conduct sample design, large-scale production, and quality control. At present, the traditional texture analysis of textile manufacturing mainly relies on external tools and artificial vision, which has strong subjective factors and low efficiency. With the continuous development of digital image processing technology, the traditional textile industry is gradually transitioning towards intelligence and automation. Research on detecting woven fabric weave parameters is more inclined towards automatic recognition. As for automatic recognition, cameras and machine vision are used to replace artificial vision for parameter detection of woven fabric images, which has good objectivity and improves the recognition efficiency, and woven fabric tissue information is quickly obtained. Therefore, automated identification of woven fabric parameters has become a research hotspot. There has been a lot of research on automatic recognition of woven fabric weaves nowadays, but there are still shortcomings in the segmentation and recognition of weave points. The segmentation of weave points only has high accuracy for the segmentation of ideal fabric images, while the recognition of weave points fluctuates greatly for different fabrics and different fabric images, indicating that the adaptability is not ideal.
In order to improve the accuracy and stability of recognition, the effect of Gaussian Mixture Model-EM (GMM-EM) algorithm on woven fabric weave recognition was studied. First, different fabric images were preprocessed and skew correctly to improve the subsequent segmentation of the weave points. Then, the grayscale projection method was used to locate the fabric weave points, and the grayscale co-occurrence moments of the tissue points were extracted as texture features. The texture feature data were dimensionally reduced by using principal component analysis. Finally, two kinds of common unsupervised learning were compared with the EM algorithm for Gaussian Mixture Models (EM-GMM), and four evaluation indicators of unsupervised learning were used for evaluation.
On this basis, for fabric images with warp and weft yarns not perpendicular to each other, the improved grayscale projection method in this paper was used to achieve fabric weave point positioning. The identification algorithm used in this paper was based on the Gaussian Mixture Model-EM (GMM-EM) algorithm to identify the weave chart.
The results of the four evaluation criteria indicate that the recognition algorithm proposed in this paper has high recognition accuracy and good adaptability, achieving automatic recognition of organizational points and outputting organizational charts. Compared with that of the other two clustering algorithms, the recognition performance of this algorithm has been effectively improved.

Key words: organization point segmentation, automatic identification, K-means clustering, FCM, Gaussian Mixture Models