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为了提高不同图像之间的基础矩阵估计的精度与效率,提出了一种结合单应变换和对极几何约束的基础矩阵估计算法。在不同图像上提取特征点并建立匹配关系,过滤其中明显的误匹配点,并计算单应变换矩阵,由于单应变换的内点和外点在图像之间的对应关系均符合对极几何约束,通过将单应变换矩阵与外点结合,进一步计算出基础矩阵。为了验证算法的有效性,在采集的图像数据与公开的图像数据集上分别进行实验,实验结果表明,该算法与RANSAC相比,在内点率与内点平均误差上均有一定提高,具有更好的鲁棒性和有效性。
Abstract：In order to improve the accuracy and efficiency of fundamental matrix estimation between various images, a fundamental matrix estimation method was proposed by using homography transformation and epipolar geometry. This method firstly extracted several feature points and found their matching points in pairimage. The mis-matching point pairs would be discarded during this process. A homography matrix was found by the best point pairs, thus the points which were correspondence with these point pairs could be seemed as coplanar. Since all points and their projection pixels on different image planes had to follow the epipolar geometry, the fundamental matrix was estimated by the homography matrix and its outliers. The experiments on public and private image set showed that this method could achieve a more robust and accurate estimation of fundamental matrix.
[1]SCHONBERGER J L,FRAHM J M.Structure-from-motion revisited[C]//2016 IEEE Conference on Computer Vision And Pattern Recognition.Las Vegas,2016:4104-4113．
[2]裴红星，刘金达，葛佳隆，等．图像拼接技术综述[J]．郑州大学学报(理学版)，2019,51(4):1-10,29.PEI H X,LIU J D,GE J L,et al.A review on image mosaicing techniques[J].Journal of Zhengzhou University (natural science edition),2019,51(4):1-10,29．
[3]QIAO Y J,TANG Y C,LI J S.Improved harris sub-pixel corner detection algorithm for chessboard image[C]//Proceedings of the 2013 2nd International Conference on Measurement,Information and Control.Harbin,2013:1408-1411．
[4]TRAJKOVIC'M,HEDLEY M.Fast corner detection[J].Image and vision computing,1998,16(2):75-87．
[5]WANG Z C,LI R,SHAO Z H,et al.Adaptive harris corner detection algorithm based on iterative threshold[J].Modern physics letters B,2017,31(15):1750181．
[6]LOWE D G.Distinctive image features from scale-invariant keypoints[J].International journal of computer vision,2004,60(2):91-110．
[7]KE Y,SUKTHANKAR R.PCA-SIFT:a more distinctive representation for local image descriptors[C]//Proceedings of the2004 IEEE Conference on Computer Vision and Pattern Recognition.Washington,2004:506-513．
[8]BAY H,TUYTELAARS T.Surf:speeded up robust features[J].Computer vision and image understanding,2008,110:346-359．
[9]LI H,HARTLEY R.Feature matching and pose estimation using newton iteration[J].Lecture notes in computer science (image analysis and processing-ICIAP 2005),2005,3617:196-203．
[10]刘经南，曾文宪，徐培亮．整体最小二乘估计的研究进展[J]．武汉大学学报·信息科学版，2013,38(5):505-512.LIU J N,ZENG W X,XU P L.Overview of total least squares methods[J].Geomatics and information science of wuhan university,2013,38(5):505-512．
[11]MOISAN L,STIVAL B.A probabilistic criterion to detect rigid point matches between two images and estimate the fundamental matrix[J].International journal of computer vision,2004,57:201-218．
[12]ZHOU F,ZHONG C,ZHENG Q.Method for fundamental matrix estimation combined with feature lines[J].Neurocomputing,2015,160:300-307．
[13]颜坤，刘恩海，赵汝进，等．快速鲁棒的基础矩阵估计[J]．光学精密工程，2018,26(2):462-468.YAN K,LIU E H,ZHAO R J,et al.A fast and robust method for fundamental matrix estimation[J].Optics and precision engineering,2018,26(2):461-470．
[14]SCHARSTEIN D,SZELISKI R.High-accuracy stereo depth maps using structured light[C]//2003 IEEE Conference on Computer Vision and Pattern Recognition.Madison,2003:195-202．
[15]SCHARSTEIN D,SZELISKI R,ZABIH R.A taxonomy and evaluation of dense two-frame stereo correspondence algorithms[C]//Proceedings IEEE Workshop on Stereo and Multi-baseline Vision.Kauai,2001:131-140.
基本信息:
DOI：10.13705/j.issn.1671-6841.2020209
中图分类号:TP391.41
引用信息:
[1]佟强,王紫瑶,杨大利,等.利用单应变换与对极约束的基础矩阵估计算法[J],2021,53(01):61-67.DOI:10.13705/j.issn.1671-6841.2020209.
基金信息:
国家重点研发计划项目(2017YFB1400402,2018YFB1701602);; 国家自然科学基金项目(61771022);; 北京信息科技大学学校科研基金项目(1925019)
2020-07-03
2020
2021-05-24
2021
2