针对传统图像匹配算法存在误匹配率高和双目视觉测量精度低的问题,本文提出一种基于非线性扩散与高维改进加速鲁棒特征(modified-speeded up robust features,M-SURF)描述符的双目视觉测量方法.首先改进非线性扩散模型中的PM(Perona-Malik)模型,使图像中边缘区域得以平滑而维持内部平坦区域不变,再将扩散后图像与原始图像进行差分运算,利用KAZE算法检测特征点;然后采用环形邻域构建描述符,在对Harr小波响应值进行叠加时,根据与其垂直方向响应值的正负号进行多区间划分,生成高维M-SURF描述符;最后采用Hamming距离匹配,利用随机采样一致性(random sample consensus,RANSAC)算法剔除误匹配并筛选出测量所需的匹配点对,根据平行双目视觉测量原理获取匹配点对的三维坐标即可完成测量.实验结果表明,本文提出算法的匹配正确率较传统KAZE算法提高24.09％,测量最小相对误差达到0.3756％,满足测量精度的要求.
Abstract
Aiming at high mismatching rate in the traditional image matching algorithms and low measurement accuracy of binocular vision,a binocular vision measurement method based on nonlinear diffusion and high-dimensional modified-speeded up robust features(M-SURF)descriptor is proposed in this paper.Firstly,the nonlinear diffusion Perona-Malik(PM)model is improved to smooth the edge region and maintain the internal flat region unchanged in the image.Then,the diffusion image and the original image are differential operated to obtain the differential image,and the KAZE algorithm is used to detect the feature points.Secondly,the ring neighborhood is used to construct the descriptor.When the Harr wavelet response value is superimposed,the high-dimensional M-SURF descriptor is generated by multi-interval division according to the sign of the vertical response value;Finally,Hamming distance is used to match,and random sample consensus(RANSAC)algorithm is used to eliminate mis-matching and screen out the key matching point pairs required for measurement.The measurement can be completed by obtaining the 3D coordinates of the key matching point pairs according to the principle of parallel binocular vision measurement.The experimental results show that the matching accuracy of the proposed algorithm is 24.09％higher than that of the traditional KAZE algorithm,and the minimum relative error of measurement is 0.3756％,which meets the requirements of measurement accuracy.
关键词
双目视觉/非线性扩散/KAZE算法/改进加速鲁棒特征(M-SURF)描述符/测量
Key words
binocular vision/nonlinear diffusion/KAZE algorithm/modified-speeded up robust features(M-SURF)descriptor/measurement
维普
万方数据