融合描述子的ORB-LBP特征匹配算法

ORB-LBP Feature Matching Algorithm Based on Fusion Descriptors

针对ORB算法在匹配过程中存在误匹配率高和鲁棒性差等问题,提出一种融合描述子的ORB-LBP特征匹配算法。该算法首先对输入图像构建金字塔尺度空间,在每一图层上检测oFAST关键点,提高算法的尺度不变性;然后采用图像块代替像素的方法提高LBP算法抗噪性能,同时通过选取最小值法和排序法使其具有旋转不变性;最后在生成rBRIEF-LBP描述子的过程中用128位改进LBP描述算子代替rBRIEF描述算子中方差较小的后128位,充分利用图像信息,以提高匹配正确率和鲁棒性。实验结果表明,所提算法较传统ORB算法在尺度变化、旋转和亮度变化方面的高精度匹配率和鲁棒性均有很大提高,更加满足复杂图像快速精准匹配的要求。

Aiming at the problems of high mismatching rate and poor robustness of the ORB matching algorithm,an improved ORB-LBP feature matching algorithm based on fusion descriptors is proposed.First of all,the algorithm constructs the pyramid scale space for the input image,detects the oFAST key points on each layer to improve the scale invariance of the algorithm.Then,the image block is used instead of the pixel to improve the anti-noise performance of the LBP algorithm,and the minimum value selecting and sorting are used to make its rotation invariant.Finally,in the process of generating rBRIEF-LBP descriptor,the 128-bit modified LBP descriptor is used instead of the last 128-bit of the rBRIEF descriptor with low variance,to make full use of the image information and improve the matching accuracy and robustness.The experimental results show that,the proposed algorithm has much better matching accuracy and robustness in the case of scale changing,rotating and brightness changing than the traditional ORB does,which can better satisfy the requirements of fast and accurate matching of complex images.