非线性尺度配准NLORB和亮度模板融合的方法

Image Registration NLORB Based on Nonlinear Scale Space and Blend Method with Brightness Template

视频场景的图像拼接方法需要拥有实时性、尺度旋转不变性和较好的融合效果。ORB尺度不变性较差,SIFT时间复杂度高,并且没有充分保持边缘细节,因此在非线性尺度与ORB的基础上,介绍改进的配准方法 NLORB和基于YUV亮度模板的均值坐标融合法。NLORB采用非线性尺度消除噪声并保持边缘,使用图像熵确定尺度参数,设置合适的关键点间距,多尺度空间搜寻极值点,构造稳定的ORB特征和描述向量,采用RANSAC进行匹配。通过仿真实验对比,NLORB能改善ORB尺度不变性和分布均匀性,提高匹配的成功率,亮度模板的均值坐标法加强了融合效果。

An image stitch method in video scene need to perform well in real time,scale and rotation invariance.Aiming at the issue that the ORB algorithm does not have scale invariance and SIFT algorithm with high time complexity does not respect the natural boundaries of objects,we put forward an image registration algorithm based on the nonlinear scale space and improved the ORB and a mean coordinate blend method based on YUV brightness template.Firstly,the nonlinear scale space is used to smooth noise and retain object boundaries,to set scale parameters based on image entropy and to set proper distance between feature points,so that the ORB detectors are stable.Then,maxima is searched for in scale and spatial location and descriptors are compute.Finally,the feature points are matched with hamming distance and RANSAC algorithm.The experimental results show that the improved algorithm can improve robustness,shorten time of registration and improve greatly the matching accuracy.What’s more,the mean coordinate method with brightness template enhances blend.