摘要
航空图像拼接具有较高的实时性要求,而传统的拼接特征为浮点数向量,在DSP、FPGA等嵌入式硬件平台上的处理效率不高.提出一种适合航空图像拼接的快速算法,利用ORB特征点作为匹配特征,以二进制特征向量进行特征距离计算,使特征提取与特征匹配速度大为提高.在图像配准过程中,采用次近邻过滤算法、交叉验证算法以及RANSAC估计算法,鲁棒地计算出拼接图像序列之间的单应矩阵.图像配准之后,相同像素位置不同的图像仍然存在一定的颜色偏差,通过对融合图像位置加权,利用改进的α-混合算法,将图像边缘位置信息纳入计算,使得图像能够自然融合,解决了图像拼接的边缘缝隙问题.整个算法对输入图像数据的复杂噪声干扰具有较好的抵抗能力.
Aviation image stitching requires real-time processing,while traditional key point descriptor generating a floating-point feature vector,thus the processing efficiency in embedded hardware platform such as DSP,FPGA is not satisfactory.Recently ORB key point is proposed,which has binary vector for feature descriptor,it greatly speeds up processing procedure for feature extraction and matching.In order to calculate the homography matrix between stitching sequence robustly,best of 2 nearest matcher,cross-validation and RANSAC estimation are adopted.Registered images still have some color deviation in the same pixel position.If the traditionalα-blending method is employed without consideration of the position information,the stitching artifacts at image edges which largely affect the visual effects will be produced.A position-weighted image fusion algorithm which takes the location information of image pixels into consideration is also presented in this paper,so the image can be naturally stitched and the problem of artifacts is solved.Furthermore,the proposed algorithm is insensitive to complex noise presented in input image data.
出处
《内蒙古师范大学学报（自然科学汉文版）》
CAS
北大核心
2014年第4期425-429,433,共6页
Journal of Inner Mongolia Normal University(Natural Science Edition)
基金
中航工业计算所创新基金资助项目(CXXM12072-16)
