基于空间六点不变量的道路立体目标识别方法

城市道路中常设置具有3D效果的平面路障或标志物,其具有高度的立体性和真实性,导致行人和辅助驾驶系统误判而造成严重事故,因此需要对道路立体目标进行识别,以获得真实路面情况。常见的射影不变量如交比是基于共面五点计算的,存在局限性,论文提出一种基于空间点元素的几何不变量计算方法,把空间元素的共点和共线用具有物理意义的量来表示,通过合理搭建把空间元素巧妙转化为共面关系。该几何不变量只依赖点的空间坐标,与投影视角、摄像机参数等无关。在三维造型软件SolidWorks上进行模拟实验,并用该计算方法对真实道路上的真假3D物体进行了测验,最终实测实验验证依据空间六点计算得到的几何量可用于道路立体目标的识别。     

一种新的几何约束结构及其射影不变量

几何不变量,特别是射影不变量,是基于单视点灰度图像识别三维物体的一条有效途径．但理论研究表明,只有特定的几何约束结构,才具有射影不变量．所以,研究并发现这种几何约束结构就具有十分重要的意义．该文提出了一种新的由相邻3平面上5条直线组成的几何约束结构及其所具有的射影不变量．该结构较Sugimoto提出的几何约束结构简单,可从结构同样复杂的物体中获得更多的几何不变量,有利于提高物体识别的稳定性;同时,由于该结构大量存在于由多面体组合而构成的人造物体及地面建筑物中,因此它非常适合这类物体的识别．实验验证了文中提出的几何约束结构具有不随物体成像视点改变的射影不变量．     

基于p2-不变量的透视变换下的点模式匹配方法

点模式匹配是计算机视觉和模式识别领域中的一个重要问题 .通过研究 ,在假定待匹配的两个点模式中已知有三对点整体对应的前提下 ,基于射影坐标以及对投影变换和排序变换同时保持不变的 p2 -不变量等理论 ,通过定义一种广义距离 ,给出了一种求解透视变换下 ,点数不等的两个平面点模式匹配问题的新算法 .理论分析和仿真实验表明 ,该算法是快速、有效的     

动态检测非函数依赖程序不变量

文章对函数依赖程序不变量和简单非函数依赖程序不变量动态生成理论、方法和技术进行了阐述,提出了一种新的简单非函数依赖程序不变量动态检测的方法.该方法利用数据库中提供的SQL强大查询功能,灵活多变地检测各种常见类型的简单非函数依赖程序不变量,并且可以根据用户的实际需要随时指定新的程序不变量查询条件.     

相位矩不变量

为了克服矩不变量存在的高阶矩计算不稳定和对噪声敏感的问题,提出一种构造相位矩不变量的方法．通过选取正交基构造相位函数,然后将相位函数作为模型函数生成新的矩不变量．该方法可以将矩不变量这种全局不变量与局部特征进行结合．分析和实验结果表明,该方法可以对不变量进行有效的拓展,并且可以取得优于原不变量的检索结果．     

共面多边形不变量计算方法研究

不变量的场景理解和目标识别是计算机视觉研究的一个重要领域,以往有关不 变量研究主要集中在点、直线、二次曲线等几何元素之间。在二维平面点的射影变换的基础 上,利用平面三角形面积不变量构造了三角形、四边形、五边形、六边形等共面多边形的不 变量,并提出了具体的计算方法。在此基础上通过举例分析和实验验证,证明文中所给公式 的正确性。     

局部矩不变量轮廓图像角点检测

为解决图像轮廓特征点问题,提出了基于轮廓局部矩不变量的轮廓角点检测方法,通过计算轮廓线支撑区域内中心点两侧轮廓点的不变矩特征,从而提取轮廓曲率函数.实验结果验证了该方法具有抗干扰性好、定位准等优点.     

一种基于目标边界的不变特征提取方法

提出了一种基于目标边界的不变特征提取方法。导出了用物体角点坐标表示的低阶边界矩的闭合形式,构造了基于边界矩的仿射变换不变量。该方法只需要对物体角点进行简单的代数运算,因此,该方法简单明了,计算量很小。实验结果证明了该方法的有效性。     

基于射影不变量的3D重构

我们提出了从立体图像基于射影不变量恢复深度的方法.方法的基础思想是对于立体图像利用密度段元素,引入了两个射影不变量来恢复密度段的深度信息.从这两个不变量,我们能推导立体图像中匹配的密度段对应该满足的关系.利用这个关系,密度段之间的匹配运算很容易实现.这个方法能直接地从输入图像中得到密集的和准确的深度,对于扭曲图像是鲁棒的.     

不确定图上期望最短距离的计算

研究了不确定图上的最短距离问题,提出了期望最短距离的概念,证明了该问题不存在多项式时间的算法.为了解决该问题,使用了随机采样技术获得不确定图的一些可能世界,在每个可能世界上计算有穷的最短距离,最后计算出平均值作为期望最短距离的估计值.为提高计算效率,使用了过滤条件来减少采样过程中采样的边数从而加快随机采样.在此基础上,提出了一种基于对称变量的、无偏的随机采样近似算法,并证明了与直接随机采样方法相比,该方法在不增加时间开销的同时能减小采样方差.通过真实数据上的实验表明,提出的算法在时间开销和采样方差上均明显好于直接随机采样方法.     

A Geometric Approach for the Theory and Applications of 3D Projective Invariants

A central task of computer vision is to automatically recognize objects in real-world scenes. The parameters defining image and object spaces can vary due to lighting conditions, camera calibration and viewing position. It is therefore desirable to look for geometric properties of the object which remain invariant under such changes in the observation parameters. The study of such geometric invariance is a field of active research. This paper presents the theory and computation of projective invariants formed from points and lines using the geometric algebra framework. This work shows that geometric algebra is a very elegant language for expressing projective invariants using n views. The paper compares projective invariants involving two and three cameras using simulated and real images. Illustrations of the application of such projective invariants in visual guided grasping, camera self-localization and reconstruction of shape and motion complement the experimental part.     

一种由未定标图象估计三维射影不变量的新算法

本文提出了一种由未定标图象估计三维射不变量的新算法。实验结果表明了本算法的有效性。     

Texture discrimination by projective invariants

We investigate the use of projective invariants for discriminating textured surfaces under projective transformation. A theorem is presented which shows that statistics of cross ratios of groups of four collinear points are a projective invariant. The distance between normalized vectors is used as the similarity measure for texture discrimination.     

Projectively invariant decomposition and recognition of planar shapes

An algorithm is presented for computing a decomposition of planar shapes into convex subparts represented. by ellipses. The method is invariant to projective transformations of the shape, and thus the conic primitives can be used for matching and definition of invariants in the same way as points and lines. The method works for arbitrary planar shapes admitting at least four distinct tangents and it is based on finding ellipses with four points of contact to the given shape. The cross ratio computed from the four points on the ellipse can then be used as a projectively invariant index. It is demonstrated that a given shape has a unique parameter-free decomposition into a finite set of ellipses with unit cross ratio. For a given shape, each pair of ellipses can be used to compute two independent projective invariants. The set of invariants computed for each ellipse pair can be used as indexes to a hash table from which model hypothesis can be generated Examples of shape decomposition and recognition are given for synthetic shapes and shapes extracted from grey level images of real objects using edge detection.     

基于几何不变量的图像特征识别

图像的特征识别是图像处理和识别中的一个重要问题，几何不变量作为特征的特征值在很多领域已经得到了广泛的应用。实际中，普遍采用在仿射变换及射影变换下保持不变的仿射、射影不变量作为特征值。本文根据具体图像的特点，利用4类仿射和射影不变量构成特征的特征值空间，依据4步识别策略来识别图像中的特征点，从而完成识别任务。实验表明，这4类不变量能够较好地识别出实际图像中的特征。     

Projective invariants of co-moments of 2D images

Functions of moments of 2D images that are invariant under some changes are important in image analysis and pattern recognition. One of the most basic changes to a 2D image is geometric change. Two images of the same plane taken from different viewpoints are related by a projective transformation. Unfortunately, it is well known that geometric moment invariants for projective transformations do not exist in general. Yet if we generalize the standard definition of the geometric moments and utilize some additional information from the images, certain type of projective invariants of 2D images can be derived. This paper first defines co-moment as a moment-like function of image that contains two reference points. Then a set of functions of co-moments that is invariant under general projective transformations is derived. The invariants are simple and in explicit form. Experimental results validated the mathematical derivations.     

Weighted Projective Spaces and a Generalization of Eves’ Theorem

For a certain class of configurations of points in space, Eves’ Theorem gives a ratio of products of distances that is invariant under projective transformations, generalizing the cross-ratio for four points on a line. We give a generalization of Eves’ theorem, which applies to a larger class of configurations and gives an invariant with values in a weighted projective space. We also show how the complex version of the invariant can be determined from classically known ratios of products of determinants, while the real version of the invariant can distinguish between configurations that the classical invariants cannot.     

Algebraic and geometric tools to compute projective and permutationinvariants

Studies the computation of projective invariants in pairs of images from uncalibrated cameras and presents a detailed study of the projective and permutation invariants for configurations of points and/or lines. Two basic computational approaches are given, one algebraic and one geometric. In each case, invariants are computed in projective space or directly from image measurements. Finally, we develop combinations of those projective invariants which are insensitive to permutations of the geometric primitives of each of the basic configurations     

Robust line matching through line–point invariants

This paper is about line matching by line–point invariants which encode local geometric information between a line and its neighboring points. Specifically, two kinds of line–point invariants are introduced in this paper, one is an affine invariant constructed from one line and two points while the other is a projective invariant constructed from one line and four points. The basic idea of our proposed line matching methods is to use cheaply obtainable matched points to boost line matching via line–point invariants, even if the matched points are susceptible to severe outlier contamination. To deal with the inevitable mismatches in the matched points, two line similarity measures are proposed, one is based on the maximum and the other is based on the maximal median. Therefore, four different line matching methods are obtained by combining different line–point invariants with different similarity measures. Their performances are evaluated by extensive experiments. The results show that our proposed methods outperform the state-of-the-art methods, and are robust to mismatches in the matched points used for line matching.     

Invariants of six points and projective reconstruction from threeuncalibrated images

There are three projective invariants of a set of six points in general position in space. It is well known that these invariants cannot be recovered from one image, however an invariant relationship does exist between space invariants and image invariants. This invariant relationship is first derived for a single image. Then this invariant relationship is used to derive the space invariants, when multiple images are available. This paper establishes that the minimum number of images for computing these invariants is three, and the computation of invariants of six points from three images can have as many as three solutions. Algorithms are presented for computing these invariants in closed form. The accuracy and stability with respect to image noise, selection of the triplets of images and distance between viewing positions are studied both through real and simulated images. Applications of these invariants are also presented. Both the results of Faugeras (1992) and Hartley et al. (1992) for projective reconstruction and Sturm's method (1869) for epipolar geometry determination from two uncalibrated images with at least seven points are extended to the case of three uncalibrated images with only six points