Fashion Color Forecasting by Applying an Improved Back Propagation Neural Network

Fashion color forecasting is one of the most important factors for fashion marketing and manufacturing. Several models have been applied by previous researchers to conduct fashion color forecasting. However, few convincing forecasting systems have been established. A prediction model for fashion color forecasting was established by applying an improved back propagation neural network (BPNN) model in this paper. Successive six-year fashion color palettes, released by INTERCOLOR, were used as learning information for the neural network to develop a reliable prediction model. Colors in the palettes were quantified by PANTONE color system. Additionally, performance of the established model was compared with other GM(1, 1) models. Results show that the improved BPNN model is suitable to predict future fashion color trend.     

基于改进Peirce线圈模型的色纺段彩纱纬平针织物仿真

为解决色纺段彩纱针织物颜色复杂多变和模拟难度大的问题,提出一种基于改进Peirce线圈结构模型的色纺段彩纱纬平针织物仿真方法.通过采集并处理纱线图像,提取纱线条干部分的图像信息,对纱线图像进行拼接.根据实际线圈结构图像,对Peirce线圈模型进行改进,确立模型中心线和各参数之间的关系.将纱线图像映射到线圈模型上,调整线...     

基于图像技术的迷彩面料颜色测量

颜色测量对于迷彩面料的色彩分析、产品设计、质量控制有着重要的指导意义。针对由织物组织纹理和图案的多样性造成测色难度增大的问题,提出一种迷彩面料图案分割的方法,并提取色彩信息加以分析。利用数码相机进行迷彩面料图像的采集,利用Mean Shift聚类算法将图像分割成不同颜色的色块,再用神经网络回归实现图像从RGB(red,green,blue)颜色空间到Lab颜色空间的转换,并通过正态分布法对迷彩颜色进行分析测量。试验结果表明:提出的图像处理方法可实现迷彩服面料颜色的自动分割;与Datacolor分光测色仪测色结果相比,正态分布法分析得到的平均色差不超过2。