By Jinlian Hu
Computing device know-how has remodeled textiles from their layout via to their manufacture and has contributed to major advances within the fabric undefined. desktop expertise for textiles and clothing presents an outline of those cutting edge advancements for a variety of purposes, masking subject matters together with constitution and illness research, modelling and simulation, and clothing design.
The ebook is split into 3 components. half one offers a assessment of other computer-based applied sciences appropriate for fabric fabrics, and comprises chapters on machine expertise for yarn and upholstery constitution research, disorder research and size. Chapters partly speak about modelling and simulation ideas of fibres, yarns, textiles and clothes, whereas half 3 concludes with a evaluation of computer-based applied sciences particular to clothing and clothing layout, with issues starting from 3D physique scanning to the educating of computer-aided layout to style students.
With its extraordinary editor and foreign group of professional participants, desktop know-how for textiles and clothing is a useful software for quite a lot of humans inquisitive about the fabric undefined, from designers and brands to fibre scientists and caliber inspectors.
- Provides an outline of leading edge advancements in laptop expertise for a variety of applications
- Covers constitution and disorder research, modelling and simulation and clothing design
- Themes variety from 3D physique scanning to the educating of computer-aided layout to type students
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Extra resources for Computer Technology for Textiles and Apparel
17 Attention-driven modelling for abnormality detection: (a) original image; (b) saliency map; (c) abnormal areas. 18 Attention-driven modelling for foreign matter detection: (a) original image; (b) saliency map; (c) foreign matter. considering the centre–surround contrasts in terms of visual features, including intensity, colour and orientation. 17(a) shows an original image captured from a yarn board in which there appear to be a number of thick places and neps on the board. In order to identify the fault zones, the attention-driven method is applied by comparing the yarn segments.
The board covered with the wound yarn is then examined and a visual appraisal of appearance is made based on the fuzziness, irregularity and visible foreign matter. Traditionally, the inspection is carried out by direct observation in which a skilled specialist visually compares the wound table with photographic standards labelled in Grades A, B, C and D, and then judges the quality of the yarn sample according to the standard definition. Recently, attempts have been made to replace the conventional observation method with computer vision to resolve the limitation of human vision in yarn appearance grading.
So far, most commercial systems to quantify yarn hairiness have been based on the optical principle. Barella (1983) and Barella and Manich (2002) have systematically reviewed a wide range of measurement techniques and various commercial instruments designed for measuring yarn hairiness. In addition to the traditional methods, digital analysis of high-quality images of a textile yarn can characterize hairiness. In this method, an image of high resolution or a microscope image with an appropriate magnification is usually adopted to ensure the clear presence of yarn hairs in the image.