中文摘要：

在分析BRISK和FREAK采样模式特点的基础上,提出一种兼具二者优点的采样模式.由于采样点距中心密集程度和采样点平滑范围重叠程度都会影响采样模式的性能,因此将采样模式建立在二者适度值上,以达到采样点对二进制位方差最高、点对之间关联度最低的目的;基于所提采样模式生成的描述子,结合SURF检测子构成了一个完整的特征检测描述方法.对比实验结果证明,文中生成的二进制描述子对特征识别匹配效果最好,提出的特征检测描述方法可应用于实时性要求高、内存紧凑的高质量目标识别.

英文摘要：

Through further analyzing sampling-pattern characteristics of BRISK and FREAK, we find that both sampling-point density and degree of overlapping have an influence on the specificity of descriptor. The two factors could appropriately be tuned to design an optimized sampling pattern, and map it onto the local area of keypoint with right orientation. A coarse descriptor, built by testing sampling points selected randomly on the sampling pattern, is used to learn a fine descriptor from training data. Results based on experiments of performance evaluation under two kinds testing environments have shown that the proposed binary descriptor outperforms the others. The good effectiveness of applying the proposed descriptor into application of 3D construction has proved that the proposed descriptor is robust to variety of image transformations, as well as performs well in real-time applications.