Pattern Separation Of Jacquard Waip-knitted Fabric In Wavelet Domain

Due to the factors such as weave structure, fiber and dyeing-and-finishing, jacquardwarp-knitted fabric is multifarious in patterns and exquisite in workmanship, for which thejacquard fabric is very popular and widely used in high-grade lingerie, garment accessory andfurnishing fabric. Although the function of2D drawing and process configuration in currentCAD system of warp knitting is nearly perfect, the pretreatment such as pattern separation ofjacquard fabric is done by using simple mapping tool such as lasso and magic wand. So it isan extremely repetitious, laborious and time-consuming work ranging from a few hours toseveral days, which occupies too much time and will increase production costs. In this case, todevelop a rapid, efficient and automatic pattern separation system for jacquard warp-knittedfabric is rather urgent.This paper focuses on texture characteristics and pattern separation methods of jacquardwarp-knitted fabric by means of computer image processing. The content of each chapter isbriefly introduced as follows.In Chapter1, the research purposes and significance are introduced briefly. Overseas anddomestic research statuses, the current pattern segmentation methods in warp knitting CADsystem are summarized. Then the difficulty in pattern segmentation is analyzed. The researchtopics and innovative points of this paper are proposed.In Chapter2, the machine structure, weaving mechanism and the classification ofjacquard warp-knitted fabrics are introduced. Then the underlying reason for fabric textureand noise signals of jacquard warp-knitted fabric are analyzed. Finally, an algorithm whichcan smoothen the fabric image, weaken noise signal and protect the detail information ofmarginal region is proposed.In Chapter3, the multi-resolution wavelet decomposition of the pretreated jacquardwarp-knitted fabric image is described. Firstly, the background of wavelet transform and thetwo decomposition models such as pyramid structure and tree structure are introduced briefly.Then a discriminant rule of decomposition model for jacquard warp-knitted fabric is proposed.The analysis result indicates that multi-resolution wavelet decomposition can simplify themodel jacquard fabric, lessen the computational burden, and provide the multi-level detailcharacteristic, especially the local detail characteristic.In Chapter4, this paper focuses on the modified K-means clustering algorithm inwavelet transform for jacquard warp-knitted fabric. Firstly, the traditional image segmentationmethods and the mechanism of K-means clustering are introduced. The problems oftraditional K-means clustering for jacquard warp-knitted fabric are analyzed, such as randomchoice of initial clustering center and the susceptivity to noise information of jacquard fabric.Then a modified K-means clustering algorithm is proposed, which includes waveletmulti-resolution decomposition, the optimized initial clustering center and weighting factorbased on dispersion degree. The modified K-means clustering algorithm is not only aindependent algorithm, but also plays an important role in successive chapter.In Chapter5, the concentrates on multi-resolution MRF model for pattern separation of jacquard warp-knitted fabric. Firstly, the traditional MRF model is introduced briefly.Secondly, on account of the problems of potential function, which relays too much onartificial expertise, and feature field which takes insufficient account of noise signals, thepaper proposes a multiresolution Markov random field with adaptive weighting in waveletdomain. The proposed algorithm can control the ratio of feature model energy to label modelenergy using a adaptive weighting function. Thirdly, by reason of non-causal label model andthe computation burden of iterations, the paper proposes a new pattern segmentation based onhierarchical Markov random field model. In new algorithm the label model takes into accountof not only the relationship between global and local, but also the modeling methods such asinter-scale causal model and intra-scale uncausal model. Finally, the segmentation results areobtained by SMAP parameter estimation which is a un-iterative algorithm in originalresolution scale.In Chapter6, the summary is introduced, which includes the main contributions and theproblems of the present study.