Research on cotton and flax fiber identification based on multi-scale features of the texture and Gaussian process classification

Abstract

Image-based automatic identification of the cotton and flax fibers is extremely significant for the content quantitatively assaying in the textile industry. In this paper, a fiber identification method based on multi-scale features of the texture and Gaussian Process Classification (GPC) is proposed. Firstly, the images of the fibers are collected by an optical microscope and a set of image preprocessing approaches including image enhancement, local binarization, morphological processing is utilized to extract the fibers from the background. Next, the single fiber images are analyzed by the Discrete Wavelet Transform (DWT) and obtain the multiple-scale features of the texture. Then, the Gray Level Co-occurrence Matrix (GLCM) is applied to describe the spatial distribution features. Subsequently, extract the statistical feature from the GLCM and obtain a 42- dimensional feature vector that contains the fiber texture. Finally, 2610 images are randomly divided into train set and test set, and the recognition expert system based on the GPC is trained and validated accordingly. The test results on the test set showed that the classification precision - recall for cotton and flax fibers reached 96% - 97% and 97% - 95%, respectively. The method proposed in this paper can help workers quickly identify cotton fibers and flax fibers for further work, such as calculating the blending ratio of blended fabrics.