平行线段对应的运动估计线性算法

针对图像序列特征的运动估计问题,提出一种基于平行线段对应的运动估计线性算法(parallel linesegments,PLS).线段采用点、线两要素表述模型,利用平行性,由像线段逐步恢复场景中的空间线段,再根据螺旋理论四元数法建立并求解基于空间线段两要素的线性运动约束方程,进一步建立粒子群优化算法(PSO)优化算法优...     

Three-Dimensional Reconstruction of Points and Lines with Unknown Correspondence across Images

Three-dimensional reconstruction from a set of images is an important and difficult problem in computer vision. In this paper, we address the problem of determining image feature correspondences while simultaneously reconstructing the corresponding 3D features, given the camera poses of disparate monocular views. First, two new affinity measures are presented that capture the degree to which candidate features from different images consistently represent the projection of the same 3D point or 3D line. An affinity measure for point features in two different views is defined with respect to their distance from a hypothetical projected 3D pseudo-intersection point. Similarly, an affinity measure for 2D image line segments across three views is defined with respect to a 3D pseudo-intersection line. These affinity measures provide a foundation for determining unknown correspondences using weighted bipartite graphs representing candidate point and line matches across different images. As a result of this graph representation, a standard graph-theoretic algorithm can provide an optimal, simultaneous matching and triangulation of points across two views, and lines across three views. Experimental results on synthetic and real data demonstrate the effectiveness of the approach.An erratum to this article can be found at     

Robust multi-view feature matching from multiple unordered views

This paper explores the problem of multi-view feature matching from an unordered set of widely separated views. A set of local invariant features is extracted independently from each view. First we propose a new view-ordering algorithm that organizes all the unordered views into clusters of related (i.e. the same scene) views by efficiently computing the view-similarity values of all view pairs by reasonably selecting part of extracted features to match. Second a robust two-view matching algorithm is developed to find initial matches, then detect the outliers and finally incrementally find more reliable feature matches under the epipolar constraint between two views from dense to sparse based on an assumption that changes of both motion and feature characteristics of one match are consistent with those of neighbors. Third we establish the reliable multi-view matches across related views by reconstructing missing matches in a neighboring triple of views and efficiently determining the states of matches between view pairs. Finally, the reliable multi-view matches thus obtained are used to automatically track all the views by using a self-calibration method. The proposed methods were tested on several sets of real images. Experimental results show that it is efficient and can track a large set of multi-view feature matches across multiple widely separated views.     

Matching 3-D line segments with applications to multiple-objectmotion estimation

A two-stage algorithm for matching line segments using three-dimensional data is presented. In the first stage, a tree-search based on the orientation of the line segments is applied to establish potential matches. the sign ambiguity of line segments is fixed by a simple congruency constraint. In the second stage, a Hough clustering technique based on the position of line segments is applied to verify potential matches. Any paired line segments of a match that cannot be brought to overlap by the translation determined by the clustering are removed from the match. Unlike previous methods, this algorithm combats noise more effectively, and ensures the global consistency of a match. While the original motivation for the algorithm is multiple-object motion estimation from stereo image sequences, the algorithm can also be applied to other domains, such as object recognition and object model construction from multiple views     

基于透视和正投影图像的3D运动与结构恢复

论文首次研究了由一幅正投影图像和一幅透视投影图像的特征点对应来进行刚体3D运动重建与结构恢复的问题,给出了有效的线性算法。以往的由运动恢复结构的工作主要集中于一组透视图像或一组正投影(通常为仿射)图像,文中采用了透视模型和正投影模型的组合。数据模拟实验结果显示该方法是比较有效和稳定的。     

Conic reconstruction and correspondence from two views

Conics are widely accepted as one of the most fundamental image features together with points and line segments. The problem of space reconstruction and correspondence of two conics from two views is addressed in this paper. It is shown that there are two independent polynomial conditions on the corresponding pair of conics across two views, given the relative orientation of the two views. These two correspondence conditions are derived algebraically and one of them is shown to be fundamental in establishing the correspondences of conics. A unified closed-form solution is also developed for both projective reconstruction of conics in space from two uncalibrated camera views and metric reconstruction from two calibrated camera views. Experiments are conducted to demonstrate the discriminality of the correspondence conditions and the accuracy and stability of the reconstruction both for simulated and real images     

Rank Conditions on the Multiple-View Matrix

Geometric relationships governing multiple images of points and lines and associated algorithms have been studied to a large extent separately in multiple-view geometry. The previous studies led to a characterization based on multilinear constraints, which have been extensively used for structure and motion recovery, feature matching and image transfer. In this paper we present a universal rank condition on the so-called multiple-view matrix M for arbitrarily combined point and line features across multiple views. The condition gives rise to a complete set of constraints among multiple images. All previously known multilinear constraints become simple instantiations of the new condition. In particular, the relationship between bilinear, trilinear and quadrilinear constraints can be clearly revealed from this new approach. The theory enables us to carry out global geometric analysis for multiple images, as well as systematically characterize all degenerate configurations, without breaking image sequence into pairwise or triple-wise sets of views. This global treatment allows us to utilize all incidence conditions governing all features in all images simultaneously for a consistent recovery of motion and structure from multiple views. In particular, a rank-based multiple-view factorization algorithm for motion and structure recovery is derived from the rank condition. Simulation results are presented to validate the multiple-view matrix based approach.     

Lines in one orthographic and two perspective views

We introduce a linear algorithm to recover the Euclidean motion between an orthographic and two perspective cameras from straight line correspondences filling the gap in the analysis of motion estimation from line correspondences for various projection models. The general relationship between lines in three views is described by the trifocal tensor. Euclidean structure from motion for three perspective views is a special case in which the relationship is defined by a collection of three matrices. Here, we describe the case of two calibrated perspective views and an orthographic view. Similar to the other cases, our linear algorithm requires 13 or more line correspondences to recover 27 coefficients of the trifocal tensor.     

Structure and motion estimation from apparent contours under circular motion

In this paper, we address the problem of recovering structure and motion from the apparent contours of a smooth surface. Fixed image features under circular motion and their relationships with the intrinsic parameters of the camera are exploited to provide a simple parameterization of the fundamental matrix relating any pair of views in the sequence. Such a parameterization allows a trivial initialization of the motion parameters, which all bear physical meaning. It also greatly reduces the dimension of the search space for the optimization problem, which can now be solved using only two epipolar tangents. In contrast to previous methods, the motion estimation algorithm introduced here can cope with incomplete circular motion and more widely spaced images. Existing techniques for model reconstruction from apparent contours are then reviewed and compared. Experiment on real data has been carried out and the 3D model reconstructed from the estimated motion is presented.     

Estimation of displacements from two 3D frames obtained from stereo

A method for estimating 3D displacements from two stereo frames is presented. It is based on the hypothesize-and-verify paradigm used to match 3D line segments between the two frames. In order to reduce the complexity of the method, an assumption is made that objects are rigid. The formulate a set of complete rigidity constraints for 3D line segments and integrate the uncertainty of measurements in this formation. The hypothesize-and-verify stages of the method use an extended Kalman filter to produce estimates of the displacements and of their uncertainty. The algorithm is shown to work on indoor and natural scenes. It is also shown to be easily extended to the case in which several mobile objects are present. The method is quite robust, fast, and has been thoroughly tested on hundreds of real stereo frames     

Recursive-Batch Estimation of Motion and Structure from Monocular Image Sequences

This paper addresses the issue of optimal motion and structure estimation from monocular image sequences of a rigid scene. The new method has the following characteristics: (1) the dimension of the search space in the nonlinear optimization is drastically reduced by exploiting the relationship between structure and motion parameters; (2) the degree of reliability of the observations and estimates is effectively taken into account; (3) the proposed formulation allows arbitrary interframe motion; (4) the information about the structure of the scene, acquired from previous images, is systematically integrated into the new estimations; (5) the integration of multiple views using this method gives a large 2.5D visual map, much larger than that covered by any single view. It is shown also that the scale factor associated with any two consecutive images in a monocular sequence is determined by the scale factor of the first two images. Our simulation results and experiments with long image sequences of real world scenes indicate that the optimization method developed in this paper not only greatly reduces the computational complexity but also substantially improves the motion and structure estimates over those produced by the linear algorithms.     

Reconstructing camera projection matrices from multiple pairwise overlapping views

In this work we address the problem of projective reconstruction from multiple views with missing data. Factorization based algorithms require point correspondences across all the views. In many applications this is an unrealistic assumption. Current methods that solve the problem of projective reconstruction with missing data require correspondence information across triplets of images. We propose a projective reconstruction method that yields a consistent camera set given the fundamental matrices between pairs of views without directly using the image correspondences. The algorithm is based on breaking the reconstruction problem into small steps. In each step, we eliminate as much uncertainty as possible.     

Multi-object reconstruction from dynamic scenes: An object-centered approach

In this paper, we present a new framework for three-dimensional (3D) reconstruction of multiple rigid objects from dynamic scenes. Conventional 3D reconstruction from multiple views is applicable to static scenes, in which the configuration of objects is fixed while the images are taken. In our framework, we aim to reconstruct the 3D models of multiple objects in a more general setting where the configuration of the objects varies among views. We solve this problem by object-centered decomposition of the dynamic scenes using unsupervised co-recognition approach. Unlike conventional motion segmentation algorithms that require small motion assumption between consecutive views, co-recognition method provides reliable accurate correspondences of a same object among unordered and wide-baseline views. In order to segment each object region, we benefit from the 3D sparse points obtained from the structure-from-motion. These points are reliable and serve as automatic seed points for a seeded-segmentation algorithm. Experiments on various real challenging image sequences demonstrate the effectiveness of our approach, especially in the presence of abrupt independent motions of objects.     

Applying temporal constraints to the dynamic stereo problem

An algorithm is presented for the problem of the stereopsis of time-varuing images (the dynamic stereo problem). Dynamic stereopsis is the integration of two problems; static stereopsis and temporal correspondence. Rather than finding the intersection of these problems to be more difficult, it was found that by solving the two problem simultaneously, and thus incorporating the spatio-temporal context within which a scene exists, some of the hard subproblems belonging to stereopsis and temporal correspondence could be avoided. The algorithm relies on a general smoothness assumption to assign both disparity and temporal matches. A simple model of the motion of three-dimensional features is used to guide the matching process and to identify conditional matches which violate a general smoothness assumption. A spatial proximity rule is used to further restrict possible matches. The algorithm has been tested on both synthetic and real input sequences. Input sequences were chosen from three-dimensional moving light displays and from “real” grey-level digitized images.     

Line-based face recognition under varying pose

Much research in human face recognition involves fronto-parallel face images, constrained rotations in and out of the plane, and operates under strict imaging conditions such as controlled illumination and limited facial expressions. Face recognition using multiple views in the viewing sphere is a more difficult task since face rotations out of the imaging plane can introduce occlusion of facial structures. In this paper, we propose a novel image-based face recognition algorithm that uses a set of random rectilinear line segments of 2D face image views as the underlying image representation, together with a nearest-neighbor classifier as the line matching scheme. The combination of 1D line segments exploits the inherent coherence in one or more 2D face image views in the viewing sphere. The algorithm achieves high generalization recognition rates for rotations both in and out of the plane, is robust to scaling, and is computationally efficient. Results show that the classification accuracy of the algorithm is superior compared with benchmark algorithms and is able to recognize test views in quasi-real-time     

Uncertainty propagation and the matching of junctions as featuregroupings

The interpretation of the 3D world from image sequences requires the identification and correspondences of key features in the scene. We describe a robust algorithm for matching groupings of features related to the objects in the scene. We consider the propagation of uncertainty from the feature detection stage through the grouping stage to provide a measure of uncertainty at the matching stage. We focus upon indoor scenes and match junctions, which are groupings of line segments that meet at a single point. A model of the uncertainty in junction detection is described, and the junction uncertainty under the epipolar constraint is determined. Junction correspondence is achieved through matching of each line segment associated with the junction. A match likelihood is then derived based upon the detection uncertainties and then combined with information on junction topology to create a similarity measure. A robust matching algorithm is proposed and used to match junctions between pairs of images. The presented experimental results on real images show that the matching algorithm produces sufficiently reliable results for applications such as structure from motion     

Estimation of 3D structure and motion from image corners

This paper deals with motion estimation from image corner correspondences in two cases: the orthogonal corner and the general corner with known space angles. The contribution of the paper is in three folds: First, the three-dimensional structure of a corner is recovered easily from its image by introducing a new coordinate system; second, it is shown that the one corner and two points correspondences over two views are sufficient to uniquely determine the motion, i.e., the rotation and translation; third, experiments using both simulated data and real images are conducted, which present good results.     

粒子测速系统中刚性平移运动假设条件

在对流场进行的粒子示踪测量中,针对空间运动粒子透视成像后易出现的误匹配问题,研究了粒子图像测速技术中近邻粒子平移运动假设,提出了三点式结构约束.该约束条件基于粒子图像测速技术的近邻性匹配准则和机器视觉映射原理,利用视平面内相同运动的特征平面共线的性质,对前后2帧图像中的粒子进行运动检测.最后通过仿真实验证明了该约束条件的有效性.     

基于EXIF信息的可变内参数序列图像的三维重构*

基于图像中数码相机嵌入的元数据信息,提出了一种可变内参数的序列图像重构算法,首先从序列中选择两幅图像建立初始结构,然后依次将其他图像加入当前重构结果,进一步通过集束调整来最小化序列中所有图像重投影误差,得到精确的三维重构结果,避免了复杂繁琐的自标定过程。实验结果验证了算法的有效性。     

Geometrical Analysis of Two Sets of 3D Correspondence Data Patterns for the Registration of Free-Form Shapes

The iterative closest point (ICP) algorithm represents an efficient method to establish an initial set of possible correspondences between two overlapping range images. An inherent limitation of the algorithm is the introduction of false matches, a problem that has been tackled by a variety of schemes mainly based on local invariants described in a single coordinate frame. In this paper we propose using global rigid motion constraints to deal with false matches. Such constraints are derived from geometric properties of correspondence vectors bridging the points described in different coordinate frames before and after a rigid motion. In order to accurately and efficiently estimate the parameters of interest, the Monte Carlo resampling technique is used and motion parameter candidates are then synthesised by a median filter. The proposed algorithm is validated based on both synthetic data and real range images. Experimental results show that the proposed algorithm has advantages over existing registration methods concerning robustness, accuracy, and efficiency.