Automatic pattern recognition and color separation of embroidery fabrics

Currently, there is very little literature on automatic image recognition and classification of embroidery fabrics. In today’s embroidery industry, front-end pattern-making still relies greatly on labor, using pattern-making software to carefully depict patterns and images in different colors and regions. Hence, an image analysis system that can recognize colors, regions and patterns automatically is a critical technique of improving the competitiveness of the embroidery industry. In this paper, the mean filtering method, central-weighted median filtering method and morphological operation were employed to filter out the light variation on the embroidery fabric surface structure, and a genetic algorithm was applied to distinguish images of repeat pattern embroidery from that of nonrepeat pattern embroidery. If it is a repeat pattern, then a much smaller sized subimage would be searched in the original image for the same color components and spatial structure, which could lower the computing load of the entire image greatly and is expected to achieve the processing speed required in an online real-time system. As for nonrepeat pattern embroidery images, discrete wavelet transform was applied to acquire low-frequency subimages, which can retain important image features while improving the computing efficiency. Be it a repeat or nonrepeat pattern, after obtaining subimages, specific criteria were used to determine the exact number of clusters, and the weighted fuzzy C-means method was employed to run color separation and region separation. The experiment proved that, in regard to the color embroidery images of repeat and nonrepeat patterns, the method proposed in this paper succeeded in color and region separations with good result.