提出了一种基于灰度差分不变量区域统计直方图的快速局部特征描述算法来解决传统灰度差分不变量特征描述子计算复杂、稳定性较差且包含的信息量较少的问题。采用低阶且具有微分几何意义的灰度差分不变量描述特征点以降低特征描述子的计算复杂度，提高特征描述子的稳定性; 利用特征点邻域的灰度信息和区域信息提高特征描述子的信息含量，增强特征描述子的鲁棒性。将该算法应用于图像匹配。实验结果表明，在图像尺度缩放、旋转、模糊、亮度变化、较小视角变化和JPEG压缩等多种变换条件下，该描述子不仅能够取得较好的匹配效果，而且处理速度比尺度不换特征变换(SIFT)提高约2倍。

A fast local feature description algorithm based on the region histogram of Greyvalue Differential Invariants(GDIs) is presented to improve the traditional GDI feature descriptor with complex computation, worse stability and less information. The GDIs with the meaning of differential geometry derived from low order derivatives are used to describe a feature point to reduce the computation complexity of the feature descriptor and enhance the stability. The intensity and relative location of the feature point neighborhood are made full use of to increase the content of information and improve the robustness of the feature descriptor. Finally, the feature description is used to match images.The experiment results demonstrate that the proposed algorithm can get better matching results in the cases of image zoom, rotation, blurring, illumination varying, smaller viewpoint changes as well as JPEG compression. Furthermore, the processing speed is about twice that of the Scale Invariable Feature Transform(SIFT).