In order to establish a scientific and effective evaluation standard of colorimetry indexes for colored spun yarns, a large-area macro image acquisition system,based on digital color measurement theory,was designed and implemented to solve the problem of conventional macro image acquisition on limited vision field, providing large area macro images for extracting and analyzing chromatic features. According to the specific coloring mechanism and process of colored spun yarns,a model combined global color features with local texture features for colorimetry indexes was established,consequently. Extracted third-order color moment features and color local directional pattern features can not only make full use of the complementarity between global and local features, but also combine diversities from different color spaces. Experimental results indicate that the extracted diversity features can accurately and steadily characterize the tiny changes of colorimetry indexes,and demonstrate the effectiveness and robustness of the fusion algorithm. Meanwhile, compared with conventional spectrophotometry, the designed novel system is more adaptive and practical.%为建立科学、有效的色纺纱色度学指标测试评价标准,以纺织品数码测色理论为基础,设计并实现色纺纱大幅面微距图像采集系统,克服传统微距图像采集视野范围受限的问题,为色度学指标特征的提取与分析提供保障.重点针对大色差色纺纱特有的呈色机制与过程,建立了结合全局颜色特征与局部纹理特征的色度学指标表征模型,提取的三阶颜色矩特征和彩色局部方向模式特征,不仅利用全局与局部特征间的互补性,而且还能结合不同颜色空间的多样性.实验结果表明:对于色纺纱色度学指标的细微改变,提取的多样性特征均能准确、稳定地进行表征,验证了融合算法的有效性和鲁棒性;同时,相较于传统的分光光度法,更具有理想的适应性和实用性.
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