双目区域视差快速计算及测距算法

目的 双目测距对水面无人艇自主避障以及视觉侦察具有重要意义,但视觉传感器成像易受光照环境及运动模糊等因素的影响,基于经典Census变换的立体匹配代价计算方法耗时长,且视差获取精度差,影响测距精度。为了提高测距精度并保证算法运行速度,提出一种用于双目测距的快速立体匹配算法。方法 基于传统Census变换,提出一种新的比特串生成方法,在匹配点正方形支持窗口的各边等距各选3个像素点,共选出8个像素点,这8个像素点两两比较生成一个字节的比特串。将左右视场中的匹配点与待匹配点的比特串进行异或,得到两点的汉明距离,在各汉明距离中找到距离最小的像素点作为匹配像素点,两像素点的横坐标差为视差。本文采用区域视差计算的方法,在左右视场确定同一目标区域后进行视差提取和滤波,利用平均视差计算目标的距离。结果 本文算法与基于传统Census变换的立体匹配视差获取方法相比,在运算速度方面优势明显,时间稳定在0.4 s左右,用时仅为传统Census变换算法的1/5。在Middlebury数据集中的图像对teddy和cones上进行的算法运行时间对比实验中,本文基于Census变换改进的算法比已有的基于Census变换的匹配算法在运行时间上快了近20 s。在实际双目测距实验中,采用本文算法在1019 m范围内测距误差在5%以内,根据无人艇的运动特点和避障要求,通过分析可知该算法的测距精度可以满足低速无人艇的避障需求。结论 本文给出的基于改进Census变换的匹配算法在立体匹配速度上有大幅提高,提取目标视差用于测距,实际测距结果表明,本文算法能够满足水面无人艇的视觉避障要求。     

On 3-D scene flow and structure recovery from multiview image sequences

Two novel systems computing dense three-dimensional (3-D) scene flow and structure from multiview image sequences are described in this paper. We do not assume rigidity of the scene motion, thus allowing for nonrigid motion in the scene. The first system, integrated model-based system (IMS), assumes that each small local image region is undergoing 3-D affine motion. Non-linear motion model fitting based on both optical flow constraints and stereo constraints is then carried out on each local region in order to simultaneously estimate 3-D motion correspondences and structure. The second system is based on extended gradient-based system (EGS), a natural extension of two-dimensional (2-D) optical flow computation. In this method, a new hierarchical rule-based stereo matching algorithm is first developed to estimate the initial disparity map. Different available constraints under a multiview camera setup are further investigated and utilized in the proposed motion estimation. We use image segmentation information to adopt and maintain the motion and depth discontinuities. Within the framework for EGS, we present two different formulations for 3-D scene flow and structure computation. One formulation assumes that initial disparity map is accurate, while the other does not. Experimental results on both synthetic and real imagery demonstrate the effectiveness of our 3-D motion and structure recovery schemes. Empirical comparison between IMS and EGS is also reported.     

基于双目视觉的基准差梯度立体匹配法􀀂

因灰度相关只是从一个侧面来描述左右图像特征点区域之间的灰度相似性,没有考虑特征点之间的空间相关性,因此利用灰度间的相似性作为测量标准进行匹配,不可避免地出现误匹配,提出了在进行双目视觉立体匹配时,采用灰度相关匹配技术,提取复峰特征点作为初始匹配集,采用视差梯度有限约束优化初始匹配集.利用左右图像一对已知对应基准点,通过计算基准点与复峰集各点间的基准差梯度,采用基准差梯度极小化评判标准,确定唯一匹配,并将匹配结果确定为新的基准点以不断更新基准点,直至左(右)图像特征点匹配完毕.通过分别对一幅弱纹理实际自然图像及已知三维坐标标准件的三维重建,证实了所提方法的有效性和可靠性.     

3-D motion estimation using a sequence of noisy stereo images:models, estimation, and uniqueness results

A kinematic model-based approach for the estimation of 3-D motion parameters from a sequence of noisy stereo images is discussed. The approach is based on representing the constant acceleration translational motion and constant precession rotational motion in the form of a bilinear state-space model using standard rectilinear states for translation and quaternions for rotation. Closed-form solutions of the state transition equations are obtained to propagate the quaternions. The measurements are noisy perturbations of 3-D feature points represented in an inertial coordinate system. It is assumed that the 3-D feature points are extracted from the stereo images and matched over the frames. Owing to the nonlinearity in the state model, nonlinear filters are designed for the estimation of motion parameters. Simulation results are included. The Cramer-Rao performance bounds for motion parameter estimates are computed. A constructive proof for the uniqueness of motion parameters is given. It is shown that with uniform sampling in time, three noncollinear feature points in five consecutive binocular image pairs contain all the spatial and temporal information. Both nondegenerate and degenerate motions are analyzed. A deterministic algorithm to recover motion parameters from a stereo image sequence is summarized from the constructive proof     

基于像元集的置信传递立体匹配

为了提高立体匹配效率和克服处理区域的视差跳跃,提出了一种基于像元集的置信传递立体匹配方法。该方法首先以像素为基元,利用层次置信传递算法得到较为准确的初始视差;然后依次根据颜色和初始视差对参考图像进行分割,再利用分裂合并策略对分割后的像元集进行平面拟合,以消除颜色分割错误对匹配造成的影响;最后在拟合后的像元集空间,利用标准置信传递优化算法得到最终解。采用国际标准图像进行测试的实验结果表明,该方法的匹配效率和精度优于同类方法。     

Contour matching using epipolar geometry

Matching features computed in images is an important process in multiview image analysis. When the motion between two images is large, the matching problem becomes very difficult. In this paper, we propose a contour matching algorithm based on geometric constraints. With the assumption that the contours are obtained from images taken from a moving camera with static scenes, we apply the epipolar constraint between two sets of contours and compute the corresponding points on the contours. From the initial epipolar constraints obtained from corner point matching, candidate contours are selected according to the epipolar geometry, contour end point constraints, and contour distance measures. In order to reduce the possibility of false matches, the number of match points on a contour is also used as a selection measure. The initial epipolar constraint is refined from the matched sets of contours. The algorithm can be applied to a pair or two pairs of images. All of the processes are fully automatic and successfully implemented and tested with various real images     

平面点匹配的一点校准算法

点模式匹配是一项重要的视觉课题。对于一个平面点集,由平移和旋转并伴有一定噪声作用产生另一点集,提出一个基于一点校准的点模式快速匹配算法,并推广到带有属性点的匹配问题中。基于一点校准的点模式匹配算法,其计算复杂性为O(mn),其中m,n分别是两个点集所含点的个数,比基于两点距离近似相等的校准匹配算法,其计算复杂性为O(m2nl)(其中l为第二个点集中与第一个点集中任两个点的距离近似相等的平均个数),极大地减少了计算量。     

An intensity-based cooperative bidirectional stereo matching with simultaneous detection of discontinuities and occlusions

This paper presents a new intensity-based stereo algorithm using cooperative bidirectional matching with a hierarchical multilevel structure. Based on a new model of piecewise smooth depth fields and the consistency constraint, the algorithm is able to estimate the 3-D structure and detect its discontinuities and the occlusion reliably with low computational costs. In order to find the global optimal estimates, we utilize a multiresolution two-stage algorithm minimizing nonconvex cost functions, which is equivalent to the MAP estimation. This basic framework computing the 3-D structure from binocular stereo images has been extended to the trinocular stereo vision for a further improvement of the performance. A few examples for the binocular and trinocular stereo problems are given to illustrate the performance of the new algorithms.     

由特征点的空间位置估计运动参数

确定被观察物体和摄像机之间的三维相对运动是计算机视觉领域的一个重要课题.假定 物体上有若干特征点,它们在空间的位置已由双目视觉求得.在此假定下,本文讨论如何由特 征点空间位置的观察值来估计运动参数.现有文献中所用的目标函数导致的是一个非线性最 优化问题.本文利用凯莱(Cayley)定理提出一种新的目标函数,它导致的是一个十分简单的 线性最小二乘问题.文中还讨论了这两种目标函数之间的关系.     

Rigidity checking of 3D point correspondences under perspectiveprojection

An algorithm is described which rapidly verifies the potential rigidity of three-dimensional point correspondences from a pair of two-dimensional views under perspective projection. The output of the algorithm is a simple yes or no answer to the question “Could these corresponding points from two views be the projection of a rigid configuration?” Potential applications include 3D object recognition from a single previous view and correspondence matching for stereo or motion over widely separated views. The rigidity checking problem is different from the structure-from-motion problem because it is often the case that two views cannot provide an accurate structure-from-motion estimate due to ambiguity and ill conditioning, whereas it is still possible to give an accurate yes/no answer to the rigidity question. Rigidity checking verifies point correspondences using 3D recovery equations as a matching condition. The proposed algorithm improves upon other methods that fall under this approach because it works with as few as six corresponding points under full perspective projection, handles correspondences from widely separated views, makes full use of the disparity of the correspondences, and is integrated with a linear algorithm for 3D recovery due to Kontsevich (1993). Results are given for experiments with synthetic and real image data. A complete implementation of this algorithm is being made publicly available     

Wide baseline image registration with application to 3-D face modeling

Establishing correspondence between features in two images of the same scene taken from different viewing angles is a challenging problem in image processing and computer vision. However, its solution is an important step in many applications like wide baseline stereo, three-dimensional (3-D) model alignment, creation of panoramic views, etc. In this paper, we propose a technique for registration of two images of a face obtained from different viewing angles. We show that prior information about the general characteristics of a face obtained from video sequences of different faces can be used to design a robust correspondence algorithm. The method works by matching two-dimensional (2-D) shapes of the different features of the face (e.g., eyes, nose etc.). A doubly stochastic matrix, representing the probability of match between the features, is derived using the Sinkhorn normalization procedure. The final correspondence is obtained by minimizing the probability of error of a match between the entire constellation of features in the two sets, thus taking into account the global spatial configuration of the features. The method is applied for creating holistic 3-D models of a face from partial representations. Although this paper focuses primarily on faces, the algorithm can also be used for other objects with small modifications.     

Performance evaluation of scene registration and stereo matchingfor cartographic feature extraction

Three major areas in the development of competent 3-D scene interpretation system are discussed. First, the importance of accurate automatic scene registration and the difficulty in automated extraction and matching of scene reference points are described. Second, the authors describe two stereo matching algorithms, S1, which is an area-based matcher previously used in the SPAM system, and S2, which is a feature-based matching algorithm based on hierarchical waveform matching. Third, the authors introduce several performance evaluation metrics that made it possible to measure the quality of the overall scene recovery, the building disparity estimate, and the quality and sharpness of the building delineations. Such manually generated scene reference models are critical for understanding strengths and weaknesses of various matching algorithms and in the incremental development of improvements to existing algorithms. Experiments were performed on difficult examples of aerial imagery     

基于几何特征的血管快速重建算法

针对X光造影图像中血管三维重建的特殊性,提出了基于血管几何特征的快速重建算法。该算法依据双目视觉的重构原理,按照一定的顺序,选择并匹配具有分叉,转折,末梢等几何特征的点作控制点。使得连接控制点的血管可以简化成一条具有一定宽度的线段。提取并生成各个控制点的三维信息,然后采用“化曲为直,多线逼近”的方法进行三维绘制。达到了快速、简便、数据量小、精度可控性高的效果。非常适合成像质量有限的X光造影图像中血管这类线形几何体的重建。最后在微机平台上,通过实际测试验证了上述优点。     

Distance learning for similarity estimation

In this paper, we present a general guideline to find a better distance measure for similarity estimation based on statistical analysis of distribution models and distance functions. A new set of distance measures are derived from the harmonic distance, the geometric distance, and their generalized variants according to the Maximum Likelihood theory. These measures can provide a more accurate feature model than the classical Euclidean and Manhattan distances. We also find that the feature elements are often from heterogeneous sources that may have different influence on similarity estimation. Therefore, the assumption of single isotropic distribution model is often inappropriate. To alleviate this problem, we use a boosted distance measure framework that finds multiple distance measures which fit the distribution of selected feature elements best for accurate similarity estimation. The new distance measures for similarity estimation are tested on two applications: stereo matching and motion tracking in video sequences. The performance of boosted distance measure is further evaluated on several benchmark data sets from the UCI repository and two image retrieval applications. In all the experiments, robust results are obtained based on the proposed methods.     

一种新的基于散焦图像的深度恢复算法

由散焦图像求深度是计算机视觉中一个非常重要的课题。散焦图像中点的模糊程度随物体的深度而变化,因此可以利用散焦图像估计物体的深度信息,该方法不存在立体视觉和运动视觉中对应点的匹配问题,具有很好的应用前景。研究了一种基于散焦图像空间的深度估计算法：将散焦成像描述成热扩散过程,借助形变函数将两幅散焦图像扩张成一个散焦空间,再估计出形变参数,进而恢复物体的深度信息。最后利用实验验证了算法的有效性。     

基于未标定彩色图像三维重建的立体匹配算法

提出了一种用于三维重建的未标定彩色图像立体匹配算法。针对Harris角点的聚簇现象,引入了限制因子来消除,通过采用精度较高的邻域彩色相关法进行匹配。针对相机平移旋转较小的情况,提出了利用抖率一致性剔除明显的误匹配,然后运用随机抽样算法估计基础矩阵,同时进一步剔除误匹配点对,最后用基础矩阵引导未匹配特征点进行匹配。实验表明,该算法用于三维重建,能够较好地恢复物体的结构,是一种有效的匹配算法。     

3-D shape reconstruction in an active stereo vision system using genetic algorithms

The recovery of 3-D shape information (depth) using stereo vision analysis is one of the major areas in computer vision and has given rise to a great deal of literature in the recent past. The widely known stereo vision methods are the passive stereo vision approaches that use two cameras. Obtaining 3-D information involves the identification of the corresponding 2-D points between left and right images. Most existing methods tackle this matching task from singular points, i.e. finding points in both image planes with more or less the same neighborhood characteristics. One key problem we have to solve is that we are on the first instance unable to know a priori whether a point in the first image has a correspondence or not due to surface occlusion or simply because it has been projected out of the scope of the second camera. This makes the matching process very difficult and imposes a need of an a posteriori stage to remove false matching.In this paper we are concerned with the active stereo vision systems which offer an alternative to the passive stereo vision systems. In our system, a light projector that illuminates objects to be analyzed by a pyramid-shaped laser beam replaces one of the two cameras. The projections of laser rays on the objects are detected as spots in the image. In this particular case, only one image needs to be treated, and the stereo matching problem boils down to associating the laser rays and their corresponding real spots in the 2-D image. We have expressed this problem as a minimization of a global function that we propose to perform using Genetic Algorithms (GAs). We have implemented two different algorithms: in the first, GAs are performed after a deterministic search. In the second, data is partitioned into clusters and GAs are independently applied in each cluster. In our second contribution in this paper, we have described an efficient system calibration method. Experimental results are presented to illustrate the feasibility of our approach. The proposed method yields high accuracy 3-D reconstruction even for complex objects. We conclude that GAs can effectively be applied to this matching problem.     

基于外极线约束的快速精确立体匹配算法

针对三维运动捕捉系统标记点匹配中存在标记点交叠等情况和速度要求,提出利用平行相机结构,使用统计匹配像素点的平均高度差计算相机间的高度差,并将其与外极线约束相结合将匹配标记点搜索范围限制在一个特定区域内,与传统的利用外极线约束需要在整条直线上进行搜索相比,将搜索范围限制在一个估算点周围,减小了搜索区域,提高了匹配速度。对于标记点存在交叠的情况,使用聚类的方法和加权距离最小值得到正确的匹配标记点,从而提高了立体匹配的精度。实验结果验证了算法的有效性。     

基于SIFT特征描述子的立体匹配算法

目前,立体匹配是计算机视觉领域最活跃的研究课题之一。为了克服传统的局部特征匹配算法对噪声和图像灰度的非线性变换敏感的缺点,本文提出了一种新的基于SIFT(Scale Invariant Feature Transform)特征描述子的立体匹配算法。该算法利用图像梯度信息,构造基于三维梯度方向直方图的SIFT特征描述子作为区域特征描述符,通过立体视觉理论中的极线约束将匹配特征的搜索空间从二维降到一维,最后以基于特征描述子欧氏距离的最近邻匹配得到匹配结果。实验结果表明,该方法匹配精度高,对图像灰度的非线性变换比较鲁棒,可以应用于对匹配算法鲁棒性要求比较高的立体视觉系统中。