Printed fabrics usually have multiple colors and intricate patterns, which make it difficult to directly measure the colors of the printed fabrics with a traditional spectrophotometer. However, a hyperspectral imaging system (HIS) can measure multiple colors since it acquires the spectral reflectance of a continuous band at every point of the fabric. For multiple-color printed fabrics, color segmentation is also very important. In this paper, color measurement of printed fabrics using the HIS was implemented; an algorithm which combines the self-organizing map (SOM) algorithm and the density peaks clustering (DPC) algorithm was then proposed to automatically determine the number of colors on the printed fabric and accurately segment the color regions for measurement. Firstly, the SOM algorithm was used to identify the main clusters, the DPC algorithm with Silhouette Index was then used to identify the optimal number of colors and merge the clusters. Experimental results show that this algorithm not only automatically determines the optimal number of colors for printed fabric and achieves accurate color segmentation, but requires less time for execution.

This article proposed a novel approach to color measurement of a single yarn using hyperspectral imaging system (HIS). Due to the size of a single yarn, it is impossible for spectrophotometers to measure its color directly. The HIS can acquire the spectral reflectance of continuous bands within a region of interest on a yarn sample, which can achieve color measurement of a single yarn compared with traditional spectrophotometers. A single yarn is segmented from the background by a spectral matching method through adaptively setting threshold of Fréchet distance values. The spectral reflectance of single yarn is specified by a method that lightness of pixels used as weight. The experiment based on Pantone Cotton Chip Set shows that the interinstrument agreement between the HIS and a standard spectrophotometer Datacolor SF650 has a significant improvement after using the R‐Model, and the average percentage improvement of the color difference is up to 54.99%. The yarn segmentation comparative experimental results show that the proposed method to segment single yarn from background is better in retaining the edge information of the yarn than the modified K‐means clustering method, and the color of the yarn segmented by the proposed method is more similar to the actual color of single yarn.