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This paper aims to overview the current status of development and application of digital image processing technology used for the yarn hairiness evaluation.

Digital image processing technology is one of the new methods used for the yarn detection, which can be used for the digital characterization and objective evaluation of yarn appearance. The comparison between the traditional detection methods and this new developed method was made and analyzed.

Compared with the traditional methods, image-based methods have the advantages of being objective, fast and accurate. Therefore, it was proved that digital image processing techniques should be a new trend in terms of the yarn appearance evaluation.

The purpose of this paper is introducing the image processing technology used for fabric analysis, which has the advantages of objective, digital and quick response.

This paper briefly describes the key process and module of some typical automatic recognition systems for fabric analysis presented by previous researchers; the related methods and algorithms used for the texture and pattern identification are also introduced.

Compared with the traditional subjective method, the image processing technology method has been proved to be rapid, accurate and reliable for quality control.

The future trends and limitations in the field of weave pattern recognition for woven fabrics have been summarized at the end of this paper.

This paper presents an overview of the yarn appearance evaluation methods based on image processing technology in recent years. A comparison of these new digital solutions and the traditional yarn appearance evaluation methods is made in detail with an introduction of the related systems, algorithms and principles. The future development of yarn appearance evaluation is also analyzed. Yarn hairiness, as the key parameter of yarn appearance, is the focus of evaluating yarn appearance. The reported methods can be classified into traditional test methods and digital image methods. The former include four main methods (weighting, electrostatic, microscopic counting, and photoelectric measurement methods). Both the advantages and disadvantages of the traditional test methods and digital image methods have been summarized and discussed in this paper. The aim of this work is to provide a good reference platform for researchers to provide knowledge on the history of the measurement methods of yarn appearance and utilize these methods to evaluate yarn appearance.

Cloth has the nature of three-dimensional, flexible and non-rigid, some traditional methods based on 2D imaging and contact-type 3D surface measurement methods can not work for this kind of material. In this paper we present a study of digitalization of three dimensional cloth appearances based on stereo vision method. The 3D stereo system includes a set of mirror group (3D lens) and a digital camera with high resolution. Based on the camera geometry and stereo matching techniques, the depth map of cloth could be calculated so as to evaluate the 3D appearance of fabrics and related objects. The results show it is effective and workable to use this system to study the cloth/garment's 3D deformation or draping profile.

This paper presents a digital imaging method based on silhouette image analysis to visualize the 3D surface roughness of textile or other flexible materials. A prototype of imaging system consisting of five components was developed: visible lighting source (white backlighting), sample stage, progressive sample feeding equipment, detector (CCD camera) and software. A sequence of silhouette images of textile surface patches could be digitalized progressively when the sample passes through the sharp edge of crossbeam in bending status, so that the whole 3D surface profile could be generated by the combination of silhouette height profile of each surface patch in sequence. This non-destructive testing method is effective for surface roughness characterization or defects detection of flexible materials; it could reconstruct a pure 3D surface profile of textile surface while discarding the information of surface color so as to avoid the fusion of shadows and dark colors, which is impossible to be implemented based on the traditional 2-D image analysis.