Projective reconstruction and invariants from multiple images

This correspondence investigates projective reconstruction of geometric configurations seen in two or more perspective views, and the computation of projective invariants of these configurations from their images. A basic tool in this investigation is the fundamental matrix that describes the epipolar correspondence between image pairs. It is proven that once the epipolar geometry is known, the configurations of many geometric structures (for instance sets of points or lines) are determined up to a collineation of projective 3-space 𝒫³ by their projection in two independent images. This theorem is the key to a method for the computation of invariants of the geometry. Invariants of six points in 𝒫³ and of four lines in 𝒫³ are defined and discussed. An example with real images shows that they are effective in distinguishing different geometrical configurations. Since the fundamental matrix is a basic tool in the computation of these invariants, new methods of computing the fundamental matrix from seven-point correspondences in two images or six-point correspondences in three images are given     

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.     

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.     

Invariant computations for analytic projective geometry

This paper focuses on two underlying questions for symbolic computations in projective geometry:

I How should a projective geometric property be written analytically? A first order formula in the language of fields which expresses a “projective geometric property” is translated, by an algorithm, into a restricted class of formulas in the analytic geometric language of brackets (or invariants). This special form corresponds to statements in synthetic projective geometry and the algorithm is a basic step towards translation back into synthetic geometry.

II How are theorems of analytic geometry proven? Axioms for the theorems of analytic projective geometry are given in the invariant language. Identities derived form Hubert's Nullstellensatz then play a central role in the proof. Prom a proof of an open theorem about “geometric properties”, over all fields, or over ordered fields, an algorithm derives Nullstellensatz identities — giving maximal algebraic simplicity, and maximal information in the proof.

The results support the proposal that computational analytic projective geometry should be carried out directly with identities in the invariant language.     

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．     

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.     

Model-Based Object Recognition Using Geometric Invariants of Points and Lines

In this paper, we derive new geometric invariants for structured 3D points and lines from single image under projective transform, and we propose a novel model-based 3D object recognition algorithm using them. Based on the matrix representation of the transformation between space features (points and lines) and the corresponding projected image features, new geometric invariants are derived via the determinant ratio technique. First, an invariant for six points on two adjacent planes is derived, which is shown to be equivalent to Zhu's result [1], but in simpler formulation. Then, two new geometric invariants for structured lines are investigated: one for five lines on two adjacent planes and the other for six lines on four planes. By using the derived invariants, a novel 3D object recognition algorithm is developed, in which a hashing technique with thresholds and multiple invariants for a model are employed to overcome the over-invariant and false alarm problems. Simulation results on real images show that the derived invariants remain stable even in a noisy environment, and the proposed 3D object recognition algorithm is quite robust and accurate.     

Projective Splines and Estimators for Planar Curves

Recognizing shapes in multiview imaging is still a challenging task, which usually relies on geometrical invariants estimations. However, very few geometric estimators that achieve projective invariance have been devised. This paper proposes a projective length and a projective curvature estimators for plane curves, when the curves are represented by points together with their tangent directions. In this context, the estimations can be performed with only three point-tangent samples for the projective length and five samples for the projective curvature. The proposed length and curvature estimator are based on projective splines built by fitting logarithmic spirals to the point-tangent samples. They are projective invariant and convergent.     

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.     

Model-based recognition of 3D objects from single images

In this work, we treat major problems of object recognition which have received relatively little attention lately. Among them are the loss of depth information in the projection from a 3D object to a single 2D image, and the complexity of finding feature correspondences between images. We use geometric invariants to reduce the complexity of these problems. There are no geometric invariants of a projection from 3D to 2D. However, given certain modeling assumptions about the 3D object, such invariants can be found. The modeling assumptions can be either a particular model or a generic assumption about a class of models. Here, we use such assumptions for single-view recognition. We find algebraic relations between the invariants of a 3D model and those of its 2D image under general projective projection. These relations can be described geometrically as invariant models in a 3D invariant space, illuminated by invariant “light rays,” and projected onto an invariant version of the given image. We apply the method to real images     

共形几何代数与几何不变量的代数运算

几何不变量的使用是计算机视觉和图形学的一个重要手段.发现一个不变量后,如何找到它与其他不变量的关系,是实际应用中的一个重要问题,这种关系的探讨主要依靠在不变量层次上的代数运算.文中介绍了共形几何代数中的基本、高级和有理不变量如何在几何问题中自然出现,它们之间如何进行代数运算,以及如何通过不变量的化简,自然地得到几何条件的充分必要化和几何定理的完全化.几何定理的机器证明作为几何定理完全化的副产品,被发展成几何定理的关系定量化,这种量化的几何还原就是几何定理的自然推广.几何不变量之间的几何关系的计算是这些技术的一个具体应用.     

计算射影与排列不变量的新方法

给出了一种表示和计算离散有限点集的射影与排列不变量的简单有效方法.该不变量在计算机视觉、模式识别中有重要应用.首先导出了射影直线上4个点的基于一种对称函数的射影与排列不变量,该不变量等于这4个点的某个原始交比值,具有计算量低,不丢失分辨力等优点.然后根据这个简单的对称函数,结合基本的多项式对称函数,推导出了平面上5个点的两个函数无关的射影与排列不变量,以及空间中6个点的3个函数无关的射影与排列不变量.     

一种基于投影不变量的目标跟踪方法

提出了一种基于平面投影不变量的目标跟踪算法．算法从图像中提取直线边缘计算投影不变量，用于对目标建模并跟踪．为提取直线边缘，使用改进的序列细化算法将边缘细化为单像素宽，而后用一种快速曲率估计方法估算边缘点的曲率，并保留估算值很小（约等于零）的点拟合直线．在所得直线族中按照邻近规则或者窗口规则挑选直线计算投影不变量．图像处理实验给出了用文中提出的图像预处理算法获得的直线边缘效果，并通过使用所得直线计算不变量的值衡量了所得不变量的稳定性和视角不变性．跟踪实验检验了跟踪算法的鲁棒性和实用性．     

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     

基于射影不变量的三维重构

文中提出了利用射影不变量来求解基于图像对三维深度恢复问题。方法的基本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,能推导立体图像中匹配的密度段对所满足的关系。利用这个关系,实现了密度段之间的匹配运算。这个方法能直接地从输入图像中得到密集和准确的深度,对变形的图像具有鲁棒性。     

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...     

Fundamental limitations on projective invariants of planar curves

In this paper, some fundamental limitations of projective invariants of non-algebraic planar curves are discussed. It is shown that all curves within a large class can be mapped arbitrarily close to a circle by projective transformations. It is also shown that arbitrarily close to each of a finite number of closed planar curves there is one member of a set of projectively equivalent curves. Thus a continuous projective invariant on closed curves is constant. This also limits the possibility of finding so called projective normalisation schemes for closed planar curves