中文摘要：

针对尺度不变特征变换（SIFT）算法计算复杂度高和匹配速度慢的难题，提出一种新的基于局部二进制模式（LBP）的尺度不变特征变换算法．首先采用高斯差分尺度空间检测局部极大值，利用圆形邻域统计梯度方向直方图来确定特征点的主方向，再通过坐标轴旋转避免图像旋转的计算代价；然后运用改进后的LBP算子求取特征点邻域的纹理信息，得到132比特的特征点描述子，有效地降低了描述子的计算复杂度；最后运用逻辑与运算对描述子进行特征点匹配．图像匹配实验结果表明，该算法具有尺度不变性、旋转不变性、仿射不变性和光照不变性等优良特性，在保证匹配正确率与SIFT和CSLBP算法基本一致的情况下，运算速度优于以上2种算法，其中光照不变性明显优于SIFT算法．

英文摘要：

In order to solve the problem of high computational complexity and the low matching speed of the traditional SIFT algorithm, a novel image matching algorithm based on LBP descriptor is proposed. Firstly, the local maxima is detected with DOG scale space as candidate interesting points. Secondly, aiming at avoiding rotating the image, the main orientations are determined statistically according to the oriented gradients histograms in circular neighborhood around the interesting point. And then, the 132-bit descriptor is structured by extracting the texture information of the interesting point neighborhood with the proposed speedy LBP descriptor named S-LBP. The computational complexity of the descriptor is reduced remarkably with the implementation of the proposed speedy I.BP （S-LBP） descriptor. Finally, the descriptor matching is carried out with the logical AND. The experimental results show that the proposed algorithm has the excellent features of scale invariance, rotation invariance, affine invariance and illumination invariance in the process of image matching. This method outperforms the SIFT and CS-LBP algorithm in efficiency elevation with the same matching correctness rate. In addition, the proposed method exceeds the SIFT algorithm in illumination invariance evidently.