摘要
图像特征检测在计算机视觉带动下得到了快速发展。SURF特征描述能够非常稳定快速地对图像特征进行检测和描述。RANSAC能够在inliers大于50%的条件下很好地估计出模型参数,在特征点匹配上起到了关键作用。本文利用SURF特征描述子对图像特征点进行检测和描述,然后运用交叉匹配的策略有效地消除一些错误匹配点对,然后运用RANSAC算法进行模型估计,最后使用线性加权的方式对图像进行融合。该方法利用了SURF快速检测和稳定性的特点和RANSAC算法时间复杂度小的特点进行特征点快速准确匹配,最终能够实现快速的图像拼接。
Image feature detection has been developed rapidly in the computer vision,and the SURF feature description can be very stable and fast.RANSAC can estimate the parameters of the model under the condition that the inliers is more than 50%,and it plays a key role in the feature points matching.The surf descriptor of image feature points of detection and description,and then applying the cross matching strategy effectively eliminate wrong matching points,then the use of RANSAC algorithm to estimate the model.Finally,using the linear weighted method for image fusion.This method can be used to match images in low time complexity with the the SURF feature detector and descriptor and RANSAC algorithm.
出处
《电子测量技术》
2016年第4期71-75,共5页
Electronic Measurement Technology
