Multi-scale density detection for yarn-dyed fabrics with deformed repeat patterns

Abstract

A new method combining the characteristics of macro-scale texture repeat patterns and micro-scale interwoven yarns of fabric images was proposed for yarn-dyed fabric density detection. The method was formulated in a research framework of multi-scale image processing and analysis. Firstly, a structure–texture decomposition approach was used to extract texture information and woven pattern details from the macro-scale fabric image. Secondly, a texture unit detection model was proposed to extract the texture units and to detect the yarn skewness in these texture units. Thirdly, a simple yet effective image registration method and a lightness gradient projection method were adopted to analyze the micro-scale fabric image and obtain the yarn locations in a texture unit. Finally, the average fabric density was calculated by coupling the near-regular features of texture units and yarn locations. The experiments showed that the proposed method was effective in detecting hundreds of yarns in the fabric samples and the computation time was very reasonable.