Stereo vision correspondence using a multichannel graph matching technique

A multichannel feature-based stereo vision technique is described in this paper where curve segments are used as the feature primitives in the matching process. Curve segments are extracted by tracking the zero-crossings of the left and right images. The generalized Hough transform of each curve and the curve length are used as a local feature vector in representing the distinctive characteristics of the curve segment. The feature vector of each curve segment in the left image is used as a constraint to find an instance of the same curve segment in the right image. The epipolar constraint on the centroids of the curve segment is used to limit the searching space in the right image.

A relational graph is formed from the left image by treating the centroids of the curve segments as the nodes of the graph. The local features of the curve segments are used to represent the local properties of the nodes, and the relationship between the nodes represents the structural properties of the objects in the scene. A similar graph is also formed from the right image curve segments. A graph isomorphism is then formed between the two graphs by using the epipolar constraint on the centroids, the local properties of the nodes, node assignment and the structural relationship (compatibility) between the nodes.     

A stereo vision technique using curve-segments and relaxationmatching

A multichannel feature-based stereo vision technique where curve segments are used as feature primitives in the matching process is described. The left image and the right image are filtered by using several Laplacian-of-Gaussian operators of different widths (channels). Curve segments are extracted by a tracking algorithm, and their centroids are obtained. At each channel, the generalized Hough transform of each curve segment in the left and the right image is evaluated. The epipolar constraint on the centroids of the curve segment and the channel size is used to limit the searching space in the right image. To resolve the ambiguity of the false targets (multiple matches), a relaxation technique is used where the initial scores of the node assignments are updated by the compatibility measures between the centroids of the curve segments. The node assignments with the highest score are chosen as the matching curve segments     

一种基于树结构的立体图像特征提取算法

针对立体图像匹配中的特征检测问题，提出了一种基于树结构的立体图像中边缘点、直线段、交点和凸多边形区域等特征的检测方案。该方法首先基于改进的Canny算子实现立体图像边缘的检测；基于立体图像的边缘梯度信息，文中提出一种分层并行式迭代式链码跟踪方法实现图像中直线段的检测：为提高检测速度并有利于凸多边形区域特征的检测，基于凸多边形区域的几何定义，通过提取直线段交点并采用树结构实现对已检测的相关直线段信息的遍历处理，最终得到树结构表示的凸多边形区域特征。实验证明，所提方法在结构化环境具有很好的检测精度和实时性。     

基于模型的三维物体识别

实现了一个完整的基于模型的三维物体识别系统,它可识别灰度图象中包含的物 体,如对遮挡加以限制,还可识别被遮挡的物体.该系统能实现物体的自动建模,也可先定性 识别某一物体的立体图对以获取高层知识,然后在高层知识的指导下准确地匹配立体图对中 相对应的特征.此外,还提出了利用最能表示物体特征的表面(特征面)来识别物体的方法,以 提高系统抗噪声的能力.大量实验证明,该系统具有相当的稳健性.     

Flexible and Robust Model Matching based on Association Graph for Form Image Understanding

A new method of image understanding for forms based on model matching is proposed in this paper as the basis of an OCR which can read a variety of forms. The outline of this method is described as follows. First, ruled lines are extracted from the input image of a form. After that, several lines are grouped as one to be recognised as data corresponding to a sub-form. These lines and sub-forms are both used for understanding the form, taking into account their feature attributes and the relationships between them. Each feature in the input image of a form is expected to correspond to a feature in one of the model forms, which are described as structured features. This correspondence is represented by a node in an association graph, where an arc represents compatible correspondences established on the basis of feature relationships. The best match is found as the largest maximal clique in the association graph. Experimental results show the method is robust and effective for document images of poor quality, and also for various styles of forms.     

基于直线间结构信息的立体视觉图像动态匹配方法

针对立体视觉匹配问题，介绍一种改进的动态规划图像匹配方法，它将边缘直线相似测度分为局部相似测度和全局相似测度，在后者中加入图像边缘直线之问的结构关系信息，并在动态搜索最优匹配路径的过程中利用结构关系约束删除不合理的匹配路径。仿真实验结果证明，采用该方法解决立体视觉中边缘线段的匹配问题，不仅提高了匹配的准确率，而且大大减少了匹配时间。     

Structural matching in computer vision using probabilisticrelaxation

In this paper, we develop the theory of probabilistic relaxation for matching features extracted from 2D images, derive as limiting cases the various heuristic formulae used by researchers in matching problems, and state the conditions under which they apply, We successfully apply our theory to the problem of matching and recognizing aerial road network images based on road network models and to the problem of edge matching in a stereo pair. For this purpose, each line network is represented by an attributed relational graph where each node is a straight line segment characterized by certain attributes and related with every other node via a set of binary relations     

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。     

Range sensing by projecting multiple slits with random cuts

A method for range sensing that projects a single pattern of multiple slits is described. Random dots are used to identify each slit. The random dots are given as randomly distributed cuts on each slit. Thus, each slit is divided into many small line segments. Segment matching between the image and the pattern is performed to obtain 3-D data. Using adjacency relations among slit segments, the false matches are reduced, and segment pairs whose adjacent segments correspond with each other are extracted and considered to be correct matches. Then, from the resultant matches, the correspondence is propagated by exploiting the adjacency relationships to get an entire range image     

Image Retrieval With Relevance Feedback Based on Graph-Theoretic Region Correspondence Estimation

This paper presents a graph-theoretic approach for interactive region-based image retrieval. When dealing with image matching problems, we use graphs to represent images, transform the region correspondence estimation problem into an inexact graph matching problem, and propose an optimization technique to derive the solution. We then define the image distance in terms of the estimated region correspondence. In the relevance feedback steps, with the estimated region correspondence, we propose to use a maximum likelihood method to re-estimate the ideal query and the image distance measurement. Experimental results show that the proposed graph-theoretic image matching criterion outperforms the other methods incorporating no spatially adjacent relationship within images. Furthermore, our maximum likelihood method combined with the estimated region correspondence improves the retrieval performance in feedback steps.     

Line segment matching for 3D computer vision using a new iteration scheme

A new iteration scheme is proposed to solve the line segment matching problem in stereo vision analysis. A match function which directly reflects the requirements of the epipolar and disparity constraints is proposed for line segment matching. The information contained in the match function is used to determine line segment correspondences indirectly. After a match network is established according to the match function values, a new iteration algorithm is employed to tune the strengths of the match links in the match network so that the match network can converge to a stable state. No explicit compatibility coefficient need be defined for computing the support function values in the iterations, resulting in a faster computation speed than those of conventional relaxation matching techniques. The inherent anti-symmetric characteristic of relaxation matching for the image correspondence problem is also avoided naturally. The experimental results show that the proposed iteration scheme is effective and suitable for matching line segments even when images are complicated.     

The Geometry and Matching of Lines and Curves Over Multiple Views

This paper describes the geometry of imaged curves in two and three views. Multi-view relationships are developed for lines, conics and non-algebraic curves. The new relationships focus on determining the plane of the curve in a projective reconstruction, and in particular using the homography induced by this plane for transfer from one image to another. It is shown that given the fundamental matrix between two views, and images of the curve in each view, then the plane of a conic may be determined up to a two fold ambiguity, but local curvature of a curve uniquely determines the plane. It is then shown that given the trifocal tensor between three views, this plane defines a homography map which may be used to transfer a conic or the curvature from two views to a third. Simple expressions are developed for the plane and homography in each case.A set of algorithms are then described for automatically matching individual line segments and curves between images. The algorithms use both photometric information and the multiple view geometric relationships. For image pairs the homography facilitates the computation of a neighbourhood cross-correlation based matching score for putative line/curve correspondences. For image triplets cross-correlation matching scores are used in conjunction with line/curve transfer based on the trifocal geometry to disambiguate matches. Algorithms are developed for both short and wide baselines. The algorithms are robust to deficiencies in the segment extraction and partial occlusion.Experimental results are given for image pairs and triplets, for varying motions between views, and for different scene types. The methods are applicable to line/curve matching in stereo and trinocular rigs, and as a starting point for line/curve matching through monocular image sequences.     

Identification of unreliable segments to improve skeletonization of handwriting images

An unavoidable problem of most existing skeletonization algorithms for handwriting images is the production of undesired artifacts or pattern distortions. This paper presents a method of identifying these unreliable segments to improve the skeletons of handwriting images. In this method, a novel feature called iteration time is exploited, by which each unreliable segment can be treated as a set of points with exceptional iteration times. First, the iteration time of each skeleton point is calculated, and an undirected graph is built from the skeleton whose edges are weighted by defining a distance measurement between each pair of connected nodes based on iteration time. Then the set of unreliable segments is achieved by a graph clustering algorithm with an effective clustering quality function. Finally, the probability of two jointed reliable segments belonging to a continuous pair is estimated by a best-matched method, and a cubic B-spline interpolation is applied to reconstruct unreliable parts of the skeleton. Experimental results show that the proposed method can detect unreliable segments effectively and produce a skeleton that is closer to the original writing trajectory.     

Scanning electron microscope-based stereo analysis

In this paper we present a novel technique to analyze stereo images generated from a SEM. The two main features of this technique are that it uses a binary linear programming approach to set up and solve the correspondence problem and that it uses constraints based on the physics of SEM image formation. Binary linear programming is a powerful tool with which to tackle constrained optimization problems, especially in cases that involve matching between one data set and another. We have also analyzed the process of SEM image formation, and present constraints that are useful in solving the stereo correspondence problem. This technique has been tested on many images. Results for a few wafers are included here.     

三维图匹配算法

目的 现有的图匹配算法大多应用于二维图像,对三维图像的特征点匹配存在匹配准确率低和计算速度慢等问题。为解决这些问题,本文将分解图匹配算法扩展应用在了三维图像上。方法 首先将需要匹配的两个三维图像的特征点作为图的节点集;再通过Delaunay三角剖分算法,将三维特征点相连,则相连得到的边就作为图的边集,从而建立有向图;然后,根据三维图像的特征点构建相应的三维有向图及其邻接矩阵;再根据有向图中的节点特征和边特征分别构建节点特征相似矩阵和边特征相似矩阵;最后根据这两个特征矩阵将节点匹配问题转化为求极值问题并求解。结果 实验表明,在手工选取特征点的情况下,本文算法对相同三维图像的特征点匹配有97.56%的平均准确率;对不同三维图像特征点匹配有76.39%的平均准确率;在三维图像有旋转的情况下,有90%以上的平均准确率;在特征点部分缺失的情况下,平均匹配准确率也能达到80%。在通过三维尺度不变特征变换（SIFT）算法得到特征点的情况下,本文算法对9个三维模型的特征点的平均匹配准确率为98.78%。结论 本文提出的基于图论的三维图像特征点匹配算法,经实验结果验证,可以取得较好的匹配效果。     

基于矩形子块法的快速全局立体匹配算法

由于计算量大,采用割图法(GC,GraphCuts)来最小化立体匹配的能量函数,计算速度慢,难以满足许多方面应用的要求。为了提高立体匹配能量函数最小化的速度,文章提出了一种新的算法:矩形子块割图法(RSRGC,RectangularSubregioningGraphCuts),即将大图像分割为小矩形子块,然后再在各小矩形子块上分别利用GC最小化能量函数。实验表明,采用该方法可以大大提高立体匹配的速度,而不影响立体匹配的效果。     

Dense Features for Semi-Dense Stereo Correspondence

We present a new feature based algorithm for stereo correspondence. Most of the previous feature based methods match sparse features like edge pixels, producing only sparse disparity maps. Our algorithm detects and matches dense features between the left and right images of a stereo pair, producing a semi-dense disparity map. Our dense feature is defined with respect to both images of a stereo pair, and it is computed during the stereo matching process, not a preprocessing step. In essence, a dense feature is a connected set of pixels in the left image and a corresponding set of pixels in the right image such that the intensity edges on the boundary of these sets are stronger than their matching error (which is the difference in intensities between corresponding boundary pixels). Our algorithm produces accurate semi-dense disparity maps, leaving featureless regions in the scene unmatched. It is robust, requires little parameter tuning, can handle brightnessdifferences between images, nonlinear errors, and is fast (linear complexity).     

A new approach to corner matching from image sequence using fuzzy similarity index

Corner matching in image sequences is an important and difficult problem that serves as a building block of several important applications of stereo vision etc. Normally, in area-based corner matching techniques, the linear measures like standard cross correlation coefficient, zero-mean (normalized) cross correlation coefficient, sum of absolute difference and sum of squared difference are used. Fuzzy logic is a powerful tool to solve many image processing problems because of its ability to deal with ambiguous data. In this paper, we use a similarity measure based on fuzzy correlations in order to establish the corner correspondence between sequence images in the presence of intensity variations and motion blur. The matching approach proposed here needs only to extract one set of corner points as candidates from the left image (first frame), and the positions of which in the right image (second frame) are determined by matching, not by extracting. Experiments conducted with the help of various sequences of images prove the superiority of our algorithm over standard and zero-mean cross correlation as well as one contemporary work using mutual information as a window similarity measure combined with graph matching techniques under non-ideal conditions.     

Consistent segmentation of 3D models

This paper proposes a method to segment a set of models consistently. The method simultaneously segments models and creates correspondences between segments. First, a graph is constructed whose nodes represent the faces of every mesh, and whose edges connect adjacent faces within a mesh and corresponding faces in different meshes. Second, a consistent segmentation is created by clustering this graph, allowing for outlier segments that are not present in every mesh. The method is demonstrated for several classes of objects and used for two applications: symmetric segmentation and segmentation transfer.     

基于模糊判别的快速图切割立体匹配

针对图切割算法存在匹配时间过长的不足,提出了一种快速的图切割立体匹配方法.选用模糊逻辑提取少量的候选匹配点,然后用这些候选匹配点构造缩减的图,再进行图切割立体匹配.采用标准图像对文中方法进行测试,并与图切割立体算法和动态规划算法进行比较的结果表明,该方法较好地保留了图切割算法匹配的准确性,而运行时间仅为图切割算法的1/35.