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|Title:||Micro structure information analysis of woven fabrics||Authors:||Yu, Xiaobo||Degree:||Ph.D.||Issue Date:||2008||Abstract:||During the last few decades, computer-aided automations are increasingly introduced into the textile industry. These new automatic techniques greatly improved the manufacturing efficiency, heightened the qualities of products and reduced the manufacturing costs. Among all the research, micro structure analysis of fabrics is always a highlight. This research is to seek effective ways to extract or measure the micro features relating to fabric woven structures. This research is also called yarn-level fabric analysis because most of the woven information is represented by yarns. In this thesis, a fabric structure analysis system is introduced. This system is proposed to deal with some common problems in woven fabric analysis, such as the density measurement, woven pattern extraction and color analysis. Especially, an information encoding method using fabric woven structure is introduced. This system is an integration of modules which are independent and cooperative function units. The foundation of the system is the woven fabric model. Two kinds of fabric models, the space domain Active-Grid-Model (AGM) and the frequency domain model, will be introduced. Fabric models are explicit representations of fabric features and information in either space or frequency domain. AGM is the derivate of point map which is the most used data format in textile industry. AGM supports a generic data interface to different modules inside the system and also the external applications, such as fabric CAD system. The frequency domain model is a statistical model in 2D Fourier spectrum to represent fabric space periodical features. These two fabric models do not exist independently but closely related to each other. The frequency model can be constructed based on the AGM and some specifications of AGM can be obtained by studying features of frequency model. This dependency is the basis of some proposed algorithms, such as the significant points based satin fabric analysis. Based on the fabric models, five function modules are proposed: fabric density measurement and satin fabric analysis using frequency domain (Chapter 4), yarn extraction (Chapter 5), woven pattern extraction using AGM (Chapter 6), yarn-level color clustering (Chapter 7) and weave code (Chapter 8). Two fabric analysis methods using frequency domain are introduced: fabric density measurement and satin fabric analysis. These two methods are proposed based on three features of frequency model: fabric density principle (FDP), significant angle and significant points in Fourier spectrum. The pre-requirement to decipher the frequency features are accurate extractions. A set of fabric frequency feature extraction methods are given. These methods guarantee the interested features can be accurately extracted. Yarns are the basic units of fabrics. Detect geometry shapes of yarn are the precondition of many fabric analysis tasks, such as the woven fabric extraction and yarn-level color analysis. Two yarn location methods are introduced. One is the histogram based method and another is the significant-yarn-segment (sys) searching method. Sys searching is more stable than histogram if the fabric samples are not regular and have irregular curves or twists while histogram based method is more robust when the image condition is low. Base on the fabric active grid model (AGM), a module to extract the fabric woven pattern is introduced. This module includes a four-step method to construct the AGM. The construction of AGM makes use of results of other two modules, the fabric density measurement and the yarn locating. The yarn locating method gives the initial guess of the AGM. After the initialization, an AGM automatic adjustment scheme is produced to gather accurate geometry information of the fabric. The cross point classification based on the edge intensities, and the correction based on neighboring and color information, are introduced. The final results show that this module can efficiently extract the fabric woven patterns. The efficiency of yarn-level color clustering (YLCC) depends on four elements: accurate locating and segmentation of yarn regions, color calibration, a proper color space and an efficient color clustering method. Four solutions are proposed to deal with four key problems in YLCC. The yarn locating and AGM automatic adjustment modules give an accurate segmentation of the yarns. The reference white based color normalization is used to calibrate fabric images during the multiple sampling. The iterative segmentations can extend the 1D clustering to 3D clustering in YCbCr space and the iterative mergence can keep the integrations of independent and distinct clusters while segmenting. In past, the decoding (extraction) of fabric woven pattern is the typical task of woven fabric analysis. In this thesis, a new coding technology, the weave code technology, is introduced. This technology provides a solution to embed information into fabrics based on the fabric woven structure. A detailed introduction to the concept of weave code, the basic alphabet mapping scheme and the composition of a complete weave code system, are given.||Subjects:||Hong Kong Polytechnic University -- Dissertations.
Textile fabrics -- Mechanical properties.
Textile fabrics -- Testing.
|Pages:||243 leaves : ill. ; 31 cm.|
|Appears in Collections:||Thesis|
View full-text via https://theses.lib.polyu.edu.hk/handle/200/3504
Citations as of May 15, 2022
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