一种基于角点检测的图像密集匹配算法

提出了一种鲁棒的图像自动立体匹配算法.利用Sobel算子对图像中的像素点进行检测,若是边缘点,则使用最小同值分割吸收核方法判断该点是否为角点.在两幅待匹配的图像间计算角点的梯度大小、梯度方向及灰度等的相似度,去除无法对应的角点,建立起待匹配图像中角点的对应关系,并计算基础矩阵.对基础矩阵进行迭代,去除误配点,计算出较精确的基础矩阵.由对极几何约束,采用动态规划方法,寻找左右两幅图像在对应极线上的所有像素点之间的对应,从而建立起两幅图像间像素点的密集匹配对应关系.试验结果表明,算法效果满意.     

一种改进的人工标记点匹配方法

针对数字近景摄影测量中多摄像机与多个待匹配点处于同一平面内的特殊情况,分析了利用外极线约束的传统匹配方法匹配错误的原因,并提出了一种改进的多图人工标记点匹配方法。该方法利用基于外极线约束的传统匹配方法确定初始匹配关系,并根据图像上标记物轮廓计算出标记物空间法向,然后利用法向过滤准则剔除错误的匹配关系。实验结果表明,该方法能够有效地剔除利用外极线约束的传统匹配方法中错误的匹配关系,提高了匹配准确度。     

一种鲁棒的序列图像自动拼接方法

针对传统图像拼接方法中鲁棒性差、计算量大及自动化程度低等问题,提出一种鲁棒性高的序列图像自动拼接方法。该方法首先采用Harris角点检测算子对经Wallis滤波后的序列图像进行特征点提取,并结合Forstner算子对特征点进行精确定位。然后基于所提取的特征点,采用邻域灰度互相关法进行序列图像的特征点匹配,得到粗匹配点集,并运用RANSAC算法对粗匹配点集处理得到精匹配点集,由精匹配点集求出较高精度的基础矩阵及极线,并由极线约束引导匹配得到高精度的匹配点对,再运用双向松弛整体匹配算法进一步剔除少数位于极线上的误匹配点。最后利用所得的高精度匹配点对,求解序列图像间的仿射变换关系,并进行图像的坐标变换和融合,从而实现序列图像的自动拼接。实验结果表明,该方法拼接效果理想,鲁棒性高,整个拼接过程全自动,不需要人工干预,具有较高的实用价值。     

非标定图像的最优匹配方法

该文将特征匹配和极线几何(epipolargeometry)估计有机地结合起来,给出了一种基于组合优化的非标定图像鲁棒匹配方法。通过灰度互相关计算得到初始候选匹配,然后使用该文提出的全局极线约束和局部视差约束代价函数,利用确定性退火方法同时估计匹配关系和基础矩阵。实验结果表明,此算法具有良好的鲁棒性,能够得到接近全局最优的匹配结果。     

基于均匀角点匹配的基础矩阵估计方法

鉴于Harris角点匹配时产生的聚簇现象,引入邻近点剔除策略,可以提取到较为均匀的匹配角点.在此基础上,进一步对计算基础矩阵的8点算法进行改进,通过对匹配点进行分组求解基础矩阵,以每组得到的基础矩阵求平均值作为最终值.实验结果表明,该方法求解的基础矩阵具有较高精度.     

基于角点特征值和视差梯度约束的角点匹配

提出了一种基于角点特征值的角点匹配快速算法,并利用视差梯度约束去除误匹配的结果。首先把提取角点时得到的角点特征值作为匹配的一个约束,提高了基于灰度相关的角点粗匹配运算的速度,然后利用视差梯度约束对粗匹配的结果进行求精运算,去除误匹配的结果,实验结果证明了该算法的有效性。     

一种改进的快速图像拼接方法

为了提高图像拼接的速度,提出了一种快速的图像拼接方法.首先在SUSAN角点检测算法检测出图像角点的基础上,采用图像分块和邻近角点剔除的方法来保证图像角点分布均匀并且避免出现角点聚簇现象,利于提高拼接的精度.其次,利用灰度相关性进行特征角点的匹配并消除伪匹配.然后采用改进的RANSAC算法快速地估计变换矩阵,该算法中采用预检测的方法快速抛弃那些不是候选模型的临时模型,加快了算法的速度.最后进行颜色融合,生成无缝拼接图像.实验结果表明,该方法在得到较高精度的情况下,大大减少了运算量,提高了图像拼接的速度.     

基于极线约束的ORB特征匹配算法

图像匹配是机器视觉领域的基础核心课题,针对当前ORB (Oriented FAST and Rotated BRIEF)图像特征匹配算法虽然执行速度快,但是匹配质量不高的问题,提出一种通过极线约束来改进ORB匹配的算法。通过合理设计Hamming阈值大小来提高初始匹配点数量,采用RANSAC和8点改进法计算基本矩阵,应用极线约束剔除误匹配保留大量优质匹配点。仿真实验结果证明,算法改进后的优质匹配点数量可达原始算法的2-3倍,同时极大地提高了匹配点的质量,证明了算法的有效性。     

一种高精度估计的基础矩阵的线性算法

通过引入与余差有关的代价函数,给出了一种高精度估计基础矩阵的线性算法--加权平移算法.首先将原始输入数据加权,计算加权后数据的重心坐标,将坐标原点平移到该重心坐标,再作归一化处理.然后用8点算法求出基础矩阵F阵的8个参数,实现了F阵的高精度估计.实验结果表明,此算法具有良好的鲁棒性,且余差和对极距离都小于其他线性算法,提高了基础矩阵的精度.     

一种改进的SIFT特征反向匹配算法

对SIFT算法进行研究,根据目前在倒车辅助系统中立体匹配技术的应用,对该算法进行改进,为了解决高维空间搜索问题,在获得的特征向量匹配对上加入外极线约束条件,将搜索空间从二维图像降为一维的线性搜索,缩短匹配时间,降低了计算的复杂度,同时针对阈值增大,匹配数目增多,同时错误率增高的情况,对特征点进行反向匹配,进一步提高图像匹配的准确率。     

一种加权计算基础矩阵的高精度算法

分析了图像匹配点对基础矩阵的不同影响,引入具有明显几何意义的匹配点到对极线的距离作为匹配点的加权因子,将匹配点集合看作随机变量并将加权因子作为匹配点的概率分布。在此基础上对原始图像数据进行归一化处理,利用8点算法得到基础矩阵。大量的试验结果表明,该方法明显降低了计算余差,提高了基础矩阵的计算精度。     

Fundamental matrix estimation: A study of error criteria

The fundamental matrix (FM) describes the geometric relations that exist between two images of the same scene. Different error criteria are used for estimating FMs from an input set of correspondences. In this paper, the accuracy and efficiency aspects of the different error criteria are studied. We mathematically and experimentally proved that the most popular error criterion, the symmetric epipolar distance, is biased. It was also shown that despite the similarity between the algebraic expressions of the symmetric epipolar distance and Sampson distance, they have different accuracy properties. In addition, a new error criterion, Kanatani distance, was proposed and proved to be the most effective for use during the outlier removal phase from accuracy and efficiency perspectives. To thoroughly test the accuracy of the different error criteria, we proposed a randomized algorithm for Reprojection Error-based Correspondence Generation (RE-CG). As input, RE-CG takes an FM and a desired reprojection error value d. As output, RE-CG generates a random correspondence having that error value. Mathematical analysis of this algorithm revealed that the success probability for any given trial is 1 − (2/3)² at best and is 1 − (6/7)² at worst while experiments demonstrated that the algorithm often succeeds after only one trial.     

Estimating the fundamental matrix based on least absolute deviation

The epipolar geometry is the intrinsic projective geometry between two views, and the algebraic representation of it is the fundamental matrix. Estimating the fundamental matrix requires solving an over-determined equation. Many classical approaches assume that the error values of the over-determined equation obey a Gaussian distribution. However, the performances of these approaches may decrease significantly when the noise is large and heterogeneous. This paper proposes a novel technique for estimating the fundamental matrix based on least absolute deviation (LAD), which is also known as the L₁ method. Then a linear iterative algorithm is presented. The experimental results on some indoor and outdoor scenes show that the proposed algorithm yields the accurate and robust estimates of the fundamental matrix when the noise is non-Gaussian.     

基于极约束和边缘点检测的图像密集匹配

综合运用极约束和边缘点检测实现了两幅图像的密集匹配,真实图像实验表明,该方法能够产生非常密集的匹配点并具有较高的匹配精度。     

一种高性能SAR图像边缘点特征匹配方法

针对合成孔径雷达（Synthetic aperture radar,SAR）图像特征匹配中特征提取的不稳定性和相似度优化搜索的复杂性问题,提出了一种精确高效稳健的SAR图像边缘点集匹配方法. 首先,分析了仿射变换模型在遥感图像匹配中的适应性,并对仿射变换模型进行了参数分解;其次,提出了基于方向模板的SAR图像边缘检测算子,并利用SAR图像边缘的梯度和方向特征,建立了基于像素迁移的多源SAR边缘点集相似性匹配准则,以及图像匹配的联合相似度-联合特征均方和（Square summation joint feature,SSJF）;然后,利用改进的遗传算法（Genetic algorithm,GA）来进行相似度的全局极值优化搜索,获取变换模型参数和边缘点集的对应关系;最后,从理论上分析了本文方法的性能,并利用多幅SAR图像的匹配实验以及与原有方法的对比分析,对本文方法的性能进行了验证.     

基于图割理论和极几何约束的图像匹配算法

提出了一种基于图割理论和极几何约束的图像匹配算法。利用极几何约束对两幅待匹配图像进行矫正,使得它们的极线处在同一扫描线上,从而将二维匹配简化成一维匹配;利用基于图割的一维匹配算法获取视差数据,此时匹配中能量函数的标号是一维的,大大减少了搜索范围,并且提高了匹配的速度。     

模糊图像中的飞机识别方法

针对模糊图像中飞机识别问题,提出一种基于角点和核聚类的飞机识别方法。该方法提取图像的角点特征,对角点进行核聚类,根据聚类结果识别飞机的角点,完成飞机的定位识别。在核聚类算法中,引入有效性函数,能自适应的确定聚类数目,解决了现有核聚类算法需要事先确定聚类数的弱点。实验表明,角点特征具有位移、旋转、尺度不变性,自适应核聚类算法能准确识别模糊图像中的飞机,具有较好的鲁棒性。     

Estimation of epipolar geometry by linear mixed-effect modelling

Epipolar geometry relies on the determination of the fundamental matrix. Classical approaches for estimating the fundamental matrix assume that a Gaussian distribution exists in the errors in view of mathematical tractability. However, this assumption will not be justified when the distribution computed is not normally distributed. We propose a new approach that does not make the Gaussian assumption, and so can attain robustness and accuracy in different conditions. The proposed framework, weighted least squares (WLS), is the application of linear mixed-effect models considering the correlation between different data subsamples. It provides an unbiased estimation of the fundamental matrix after mitigating the effects of outliers. We test the new model by using synthetic and real images, and comparing it to standard methods.     

From lines to epipoles through planes in two views

This paper addresses the computation of the fundamental matrix between two views, when camera motion and 3D structure are unknown, but planar surfaces can be assumed. We use line features which are automatically matched in two steps. Firstly, with image based parameters, a set of matches are obtained to secondly compute homographies, which allows to reject wrong ones, and to grow good matches in a final stage. The inclusion of projective transformations gives much better results to match features with short computing overload. When two or more planes are observed, different homographies can be computed, segmenting simultaneously the corresponding planar surfaces. These can be used to obtain the fundamental matrix, which gives constraints for the whole scene. The results show that the global process is robust enough, turning out stable and useful to obtain matches and epipolar geometry from lines in man made environments.