Research On Recognition Of Fabric Weave Pattern Based On Space-Frequency Domain

Recognition for fabric weave pattern is one of the key issues in fabric analysis and design field. The parameters of yarn can be extracted and fabric weave pattern can be recognized by analyzing the surface, weave pattern and color distribution of the fabric sample. Scientific basis of fabric innovative design is established with the accurate weave pattern recognition, which will promote competitiveness of textile industry.Although the emergence of textile CAD system greatly simplifies the product design and weaving, automatic recognition for weave pattern has a great of difficulty. Due to the complexity of manufacturing, the final form of the fabric has much uncertain information. The recognition performances of classical methods aren't satisfying. No matter the statistical method, geometric method or spectral method and models method are all assume the fabric images are the ideal arrangement of warp and weft yarns, that the yarn gap position can be well represented. These methods are presented in condition of stationary and spatial-invariance signals. In practice, real fabric images are mostly non-stationary and spatial-variance. Considering the disadvantages of classical methods, this dissertation focuses on the key part of parameters of yarn and recognition of weave pattern.In this dissertation, several theoretical issues based on space-frequency domain are focused. The main contributions are given as follows:(1) On the study of the marginal spectrum of Hilbert-Huang transform a method is proposed to calculate fabric yarn density of tabby by analyzing Hilbert marginal spectrum of fabric sample image, and the yarn density of twill will be calculated by analyzing the Hilbert-Huang Hough transform. Experiments results show that this method is infinitely close to the value measured by manual.(2) A new fabric multi-pattern recognition proposed based on B-spline wavelet transform modulus maximum theory to recognize fabric sample point effectively, and capture gray-scale changes accurately in the fabric image. Therefore, the exact position of the yarns achieved clearly. This method is proposed to help simplify the implementation, and improve the robustness of the pattern detection, whether the fabric is single or multi-pattern.(3)A method of recognition for color fabric weave pattern is proposed based on the theory of Quaternion-Gabor and Quaternion-PCA. The color fabric weave pattern image is pre-processed using Quaternion-Gabor filter firstly, and clustering method is applied to approach initial segmentation, then color fabric weave pattern segmentation is build based on Quaternion-PCA method to implement accurate segmentation of different weave patterns and different color models.