研究了加速鲁棒特征(SURF)描述符的局部邻域划分方法, 以降低该描述符的维数, 提升基于SURF的图像匹配算法的匹配速度和鲁棒性。参考尺度不变特征变换(SIFT)描述符和SURF描述符已有的各种邻域划分方式, 将SURF描述符的局部邻域分为栅格状(原SURF划分方式)、三角形和扇形进行分析。首先, 分析了图像的尺度和旋转变化对这3种邻域划分方式描述符匹配性能的影响; 然后, 提出了构建三角形划分和扇形划分SURF描述符的方法; 最后, 进行了匹配实验, 对8种不同划分方式的SURF描述符进行了比较。结果表明: 扇形划分SURF描述符的性能要优于三角形划分和栅格划分SURF描述符, 其中6扇区、8扇区、12扇区及三角形划分的SURF描述符的性能均比SURF描述符有一定程度的提升, 描述符的维数与原SURF描述符(64维)相比分别低了40维、32维、16维和32维。

Several local region partition methods for Speeded Up Robust Features (SURF) descriptors were researched to reduce their dimensions and increase the matching speeds and robustnesses of SURF based matching algorithms. With reference to existing local region partition methods of Scale Invariant Feature Transform (SIFT) and SURF descriptors, the local regions were divided into grids(original SURF), triangles, and sectors. First, the influences of scale change and rotation of the image on the matching performance of SURF descriptors were analyzed. Then, a method to construct the SURF descriptors with local region partitions in triangles and sectors were proposed, and matching experiments were performed. The SURF descriptors with different local region partitions were compared. The experiment results show that the performance of the sector partition based SURF descriptor is better than those of triangle partition and grid partition (original SURF) based SURF descriptors. The performance of SURF descriptors with 6-sector partition, 8-sector partition, 12-sector partition and triangle partition is better than that of original one, and the dimensions of these new descriptors are 40, 32 , 16 and 32 lower than that of original SURF (64 dimensions).