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1.
图像特征匹配的准确度直接影响着图像分析与处理的效率与性能,所以要对图像的特征匹配点进行提纯和过滤。首先使用SIFT算法从图像中提取显著特征,建立粗略的匹配关系,利用最近邻比策略初始化特征匹配点的匹配概率,然后基于混合模型的最大似然估计采用EM算法建立匹配点之间的空间转换模型。EM迭代收敛之后,通过其对应关系过滤掉错误的匹配点。实验数据表明,本方法提纯的平均精度可以达到96.8%,平均召回率为81.6%,平均时间消耗为3.1 s。采用该方法提取到的正确匹配点数高于其他算法,同时对包括大视角差、光线变化和仿射变换等大多数变换具有鲁棒性。  相似文献   

2.
针对未知环境中机器人视觉导航的自然路标检测,提出了一种基于角点聚类的自然路标局部特征提取、不变性表示及其匹配算法.用SUSAN算子提取左右视图中的角点,在极线约束下对左右视图的角点进行匹配,消除遮挡或噪声引起的角点;同时应用立体视觉计算角点视差,进一步筛选角点.根据角点聚类策略提取自然路标局部特征,并提出不随距离、角度变化的局部特征不变性表示及匹配方法.理论分析和实验结果表明,该算法具有较好的鲁棒性,在一定距离和角度变换下能够对路标进行正确识别。  相似文献   

3.
刘政  刘本永 《计算机应用》2014,34(12):3554-3559
特征点匹配是基于特征点的图像配准技术中的一个重要环节。针对现有基于尺度不变特征变换(SIFT)图像配准技术特征点匹配不理想,也无法较客观、快速地筛选正确匹配点对的问题,提出结合图像深度信息进行特征点误匹配筛选剔除的方法。该算法首先根据模糊聚焦线索和机器学习算法估计出待配准图像的深度信息图,再提取SIFT特征点,并在特征点匹配环节利用随机抽样一致性(RANSAC)算法迭代循环,结合深度局部连续性的原理来进一步提高匹配精度。实验结果表明,该算法具有很好的误匹配点对剔除功能。  相似文献   

4.
通过引入能量函数,给出了一种快速高精度求取匹配点算法——基于能量的算法。首先快速求取初始匹配点,然后对初始匹配点的相邻区域的点进行能量估计,计算其判断函数值,再根据判断函数值对原始数据进行矫正,得到精确的匹配点数据,从而实现匹配点的快速高精度估计。  相似文献   

5.
李聪  赵红蕊  傅罡 《计算机应用》2014,34(10):2930-2933
考虑到只依赖对极几何关系的匹配点余差并不能完全区分匹配点的正确与否,从而影响内点集选取的情况,提出基于三视图约束的基础矩阵估计算法。首先,使用传统随机抽样一致性(RANSAC)算法计算三视图的任意两对相邻图像间的基础矩阵,确定三视图中共有的匹配点对,并计算估计基础矩阵时非共用图像上的匹配点在共用图像上的极线;然后,计算两条极线的交点与共用图像上对应匹配点间的距离,以距离值的大小作为内点判断的依据,得到新的内点集。在新内点集的基础上,采用M估计算法重新计算基础矩阵。实验结果表明:该方法可以同时降低噪声和错误匹配对基础矩阵精确计算的影响,精度优于传统鲁棒性算法,使点到极线的距离限制在0.3个像素左右,而且计算结果具有稳定性,可以被广泛地应用到基于图像序列的三维重建和摄影测量等领域中。  相似文献   

6.
基于二阶矩的SIFT特征匹配算法   总被引:1,自引:0,他引:1  
针对图像视角变化而导致的匹配率低的问题,提出基于二阶矩的SIFT特征匹配算法.算法在尺度空间检测出特征点,用仿射的二阶矩来估计特征点的椭圆邻域,把椭圆邻域梯度的主方向作为该特征点的方向,生成特征向量,最后采用欧氏距离作为度量函数进行特征向量的匹配.实验表明,改进后的算法继承了SIFT算法对图像缩放、旋转等不变性,而且增...  相似文献   

7.
在未定标系统中,对基础矩阵进行稳定估计是视图合成的关键环节。本文考虑到各匹配点由于误差不同而对基础矩阵造成的不同影响,通过在最优目标函数中引入与余差有关的权重因子提出了一种新的迭代加权线性算法。实验表明,此方法进一步提高了基础矩阵的估计精度,具有较好的稳定性且运算速度快,易于实现。  相似文献   

8.
针对目前图像配准算法对于多重复纹理图像配准位置偏差的问题,提出图像内自匹配与图像间互匹配相结合的双匹配配准(Double-match image registration, DMIR)算法。首先在对待匹配图像提取尺度不变特征转换(Scale-invariant feature transform, SIFT)特征之后,通过K-近邻(K-nearest neighbor, KNN)算法进行特征匹配,分别得到同一张图片的自匹配点对和不同图像间的初始互匹配点对;然后对初始互匹配点对进行相关性计算得到最正确的匹配点对,并根据最正确的匹配点对与自匹配点对的位置关系确定更多的正确匹配点对,最后计算仿射矩阵对图像进行拼接。实验结果显示经过DMIR算法获得的正确匹配点对更均匀、更准确,且拼接图像效果更好。  相似文献   

9.
针对ORB算法特征点提取集中、匹配精度低的问题,提出一种基于区域分块的ORB改进算法。通过区域分块的方式,提取分布均匀的特征点,使用Hamming距离对提取的特征点进行粗匹配,筛选掉误差过大的特征点,接着使用RANSAC算法对特征点进行进一步筛选,使用RANSAC算法计算出待配准图像与模板图像之间的单应性变换矩阵,对图像的方位进行纠正。通过实验对算法的可行性进行验证,结果表明,算法在兼顾运行时间的基础上对图像匹配的精度有较大的提升。  相似文献   

10.
用改进的粒子群算法实现多模态刚性医学图像的配准   总被引:6,自引:0,他引:6  
多模态医学图像的配准在医学诊断和治疗计划中起着重要的作用.提出了一种基于轮廓特征点和改进的粒子群优化算法((Particle Swarm Optimization,PSO))求解的配准方法.该方法首先用Canny算子提取图像的边缘,用ISODATA算法进行聚类分析提取出轮廓特征点,然后用两轮廓匹配点对的欧几里德距离平均值的极小值作为两个特征点对配准准则,并用改进的PSO算法求解配准所需的空间变换参数.实验证明;该方法配准精度能够达到亚像素级,能够避免陷入局部极小值而且速度得到明显改善,其应用于多模态医学图像的配准是可行的.  相似文献   

11.
常用的特征点匹配算法通常设置严苛的阈值以剔除错误匹配,但这样也会导致过多的正确匹配被删除。针对这一问题,提出了一种采用双约束的特征点匹配方法。首先,在局部上统计特征点匹配数量,运用网格对应的方法过滤部分错误匹配;然后,在全局上运用RANSAC方法计算基础矩阵,通过极线约束对匹配进行再一次筛选。实验表明,相比于传统的匹配算法,该算法能在不增加算法运行时间的前提下,获得更高数量和更高质量的匹配集合。  相似文献   

12.
分析了基于随机抽样检测思想的现有鲁棒算法在基本矩阵估计中存在的不足,结合LMedS和M估计法各自的优点,提出一种新的高精度的L-M基本矩阵估计算法。利用LMedS思想方法获得内点集,此时内点集通常情况下不包含误匹配,但仍存在位置误差,用Torr-M估计法计算基本矩阵,因为当匹配点只存在位置误差时,用M估计法得到的基本矩阵非常精确。大量的模拟实验和真实图像实验数据表明,在高斯噪声和误匹配存在的情况下,该算法具有更高的鲁棒性和精确度。  相似文献   

13.
In defense of the eight-point algorithm   总被引:11,自引:0,他引:11  
The fundamental matrix is a basic tool in the analysis of scenes taken with two uncalibrated cameras, and the eight-point algorithm is a frequently cited method for computing the fundamental matrix from a set of eight or more point matches. It has the advantage of simplicity of implementation. The prevailing view is, however, that it is extremely susceptible to noise and hence virtually useless for most purposes. This paper challenges that view, by showing that by preceding the algorithm with a very simple normalization (translation and scaling) of the coordinates of the matched points, results are obtained comparable with the best iterative algorithms. This improved performance is justified by theory and verified by extensive experiments on real images  相似文献   

14.
朱永丰  朱述龙  张静静  朱永康 《计算机科学》2016,43(Z6):198-202, 254
针对大范围室外场景和具有重复、高频纹理特征(例如水泥地、草坪)的场景,提出了一种鲁棒性强、定位精度高、速度更快的视觉定位算法。采用8级图像金字塔的ORB (Oriented FAST and Rotated BRIEF)特征描述子提取图像特征点,通过K近邻(KNN)匹配相邻图像序列的特征点对,依次解算基础矩阵F和本质矩阵E,采用自适应法利用单应矩阵和本质矩阵进行位姿估计,最后解算两帧图像间相机刚体运动的旋转R和平移t,利用三角测量法则求解出匹配点的三维坐标,重建相机运动轨迹。为了提高算法性能,提出采用最小化基于点特征的非线性重投影误差优化三维点。通过调用OpenCV在C++中实现,对所采集的数据集进行测试,测试结果表明,该方法比传统的3D位姿估计更优,实时可行。由于其基于单目而实现,因此无法得到尺度信息。  相似文献   

15.
This paper presents a novel method for addressing the problem of finding more good feature pairs between images, which is one of the most fundamental tasks in computer vision and pattern recognition. We first select matched features by Bi-matching as seed points, then organize these seed points by adopting the Delaunay triangulation algorithm. Finally, triangle constraint is used to explore good matches. The experimental evaluation shows that our method is robust to most geometric and photometric transformations including rotation, scale change, blur, viewpoint change, JPEG compression and illumination change, and significantly improves both the number of correct matches and the matching score. And the application on estimating the fundamental matrix for a pair of images is also shown. Both the experiments and the application demonstrate the robust performance of our method.  相似文献   

16.
目的 特征点匹配算法是当今计算机图像处理领域的研究热点,但是大多数现存的方法不能同时获得数量多和质量优的匹配。鉴于此,基于SURF (speeded-up robust features)算法,通过引入极线约束来提高特征匹配效果。方法 首先使用SURF算法检测和描述图像特征点,然后使用RANSAC (random sampling consensus)方法计算匹配图像之间的基础矩阵,通过该基础矩阵计算所有特征点的极线。再引入极线约束过滤掉错误匹配,最终获得数量与质量显著提高的匹配集合。结果 实验结果表明,该方法获得的匹配具有高准确度,匹配数目与原约束条件相比可高达2~8倍。结论 本文方法实现过程简单,不仅匹配准确度高且能够大大提高正确的特征匹配数,适用于处理不同类型的图像数据。  相似文献   

17.
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.  相似文献   

18.
Fundamental matrix estimation is a central problem in computer vision and forms the basis of tasks such as stereo imaging and structure from motion. Existing algorithms typically analyze the relative geometries of matched feature points identified in both projected views. Automated feature matching is itself a challenging problem. Results typically have a large number of false matches. Traditional fundamental matrix estimation methods are very sensitive to matching errors, which led naturally to the application of robust statistical estimation techniques to the problem. In this work, an entirely novel approach is proposed to the fundamental matrix estimation problem. Instead of analyzing the geometry of matched feature points, the problem is recast in the frequency domain through the use of integral projection, showing how this is a reasonable model for orthographic cameras. The problem now reduces to one of identifying matching lines in the frequency domain which, most importantly, requires no feature matching or correspondence information. Experimental results on both real and synthetic data are presented that demonstrate the algorithm is a practical technique for fundamental matrix estimation. The behavior of the proposed algorithm is additionally characterized with respect to input noise, feature counts, and other parameters of interest  相似文献   

19.
基于遗传算法不同策略下的基础矩阵估计方法   总被引:3,自引:0,他引:3  
在未定标系统中,对极几何约束给出了图像间的全部信息,成为解决许多视觉问题的关键环节,提出了一种基于遗传算法不同策略下的基础矩阵估计方法,它利用每个基因代表一个匹配点,每条染色体作为基础矩阵计算时的最小子集,并根据染色体长度决定采用何种策略估计基础矩阵,此方法在很大程度上减小了出格点对估计过程的影响,能够较好地汇聚到全局最优解,模拟数据和真实图像的实验结果都表明,所给出的方法能够有效地检测和删除错定位和误匹配点,提高了基础矩阵估计的鲁棒性和精度。  相似文献   

20.
The perspective projections of n physical points on two views (stereovision) are constrained as soon as n ≥ 8. However, to prove in practice the existence of a rigid motion between two images, more than 8 point matches are desirable in order to compensate for the limited accuracy of the matches. In this paper, we propose a computational definition of rigidity and a probabilistic criterion to rate the meaningfulness of a rigid set as a function of both the number of pairs of points (n) and the accuracy of the matches. This criterion yields an objective way to compare, say, precise matches of a few points and approximate matches of a lot of points. It gives a yes/no answer to the question: “could this rigid points correspondence have occurred by chance?”, since it guarantees that the expected number of meaningful rigid sets found by chance in a random distribution of points is as small as desired.It also yields absolute accuracy requirements for rigidity detection in the case of non-matched points, and optimal values of n, depending on the expected accuracy of the matches and on the proportion of outliers. We use it to build an optimized random sampling algorithm that is able to detect a rigid motion and estimate the fundamental matrix when the set of point matches contains up to 90% of outliers, which outperforms the best currently known methods like M-estimators, LMedS, classical RANSAC and Tensor Voting.  相似文献   

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