目的 提出一种结合C/S(Client/Server)架构和BRF(Boosted random ferns)算法的移动增强现实应用方案，以保证图像识别算法对于产品外包装的识别性能。方法 BRF是一种高效、鲁棒的特征匹配算法，但由于手机内存及处理器等硬件条件的制约，不能直接适用于手机终端。将C/S模式与BRF算法相结合应用于图像特征匹配，并设计实验测试比较文中方案(CS-BRF)与ORB算法的识别速度和匹配精度。结果 实验结果表明，相比ORB算法，CS-BRF在识别速度相近的前提下，具有更为优异的识别精度。 结论 CS-BRF能够实时准确识别印刷品图像，良好适用于产品包装移动增强现实系统。

This paper aims to put forward a mobile augmented reality scheme by combining the C/S (client/server) architecture and BRF (boosted random ferns) algorithm that can enable the recognition performance for product packaging. BRF was an effective and robust feature matching algorithm, but not suitable for mobile phones directly because of the devices’ limited capabilities. This paper combined the C/S mode and BRF algorithm for matching features, and performed experiments and compared the recognition speed and accuracy of CS-BRF and ORB. Experimental results showed that CS-BRF had close efficiency and better accuracy than ORB. In conclusion, CS-BRF can recognize printed images rapidly and precisely, and thus is well applicable to mobile augmented reality system for product packaging.