基于有向模板和颜色加权SVD的图像匹配算法

为快速稳定地匹配视频序列,并考虑SVD算法的高效性,根据视频序列的特点,对SVD匹配算法进行改进,提出了一种适合视频序列的匹配算法。该算法使用Harris角点检测算子检测兴趣点,使用有向模板提取具有旋转不变性的特征,并通过引入颜色加权法改进SVD算法中的相似性度量函数。同时,又提出一种基于运动一致性约束的误配点剔除方法,首先拟合匹配点间的运动模型,然后自适应地调整参数将错误的匹配点剔除。该算法使用有向模板消除图像间旋转变换的影响,使用颜色特征降低兴趣点匹配时的不确定性,通过运动一致性约束降低误配点数量。实验结果表明,该算法在图像间存在旋转变换关系和不同的光照条件时都可以获得很好的匹配结果,特别是在图像间基线距离较大时仍能得到大量的匹配点并具有很高的正确匹配率,能很好满足实际需要。     

基于图匹配的旋转不变弹性点匹配算法

针对旋转不变的弹性点匹配问题,提出一种基于图匹配的算法。对两点集分别构造边集合,然后定向的形状上下文距离和边长度的差别被用于度量两点集的边之间的相似性。基于边的相似性,点对应关系通过求解一个图匹配问题而恢复。实验结果表明该算法可以获得很好的配准结果并且鲁棒、高效。     

结合颜色矢量的谱匹配算法

提出2种结合颜色矢量的谱匹配算法。一种算法是从空间矢量关系的角度提取不受光源影响的图像颜色特征,结合图像特征点的几何特征,为待匹配的2幅图像分别构造亲近矩阵,通过对亲近矩阵进行奇异值分解构造一个反映特征点之间匹配程度的关系矩阵,从而获得匹配结果。另一种是将得到的匹配结果作为初始概率,通过双随机矩阵计算谱匹配概率矩阵,获得匹配的最终解。实验结果表明, 2种算法都具有较高的匹配精度。     

基于蚁群算法的多目标优化

提出2种结合颜色矢量的谱匹配算法。一种算法是从空间矢量关系的角度提取不受光源影响的图像颜色特征,结合图像特征点的几何特征,为待匹配的2幅图像分别构造亲近矩阵,通过对亲近矩阵进行奇异值分解构造一个反映特征点之间匹配程度的关系矩阵,从而获得匹配结果。另一种是将得到的匹配结果作为初始概率,通过双随机矩阵计算谱匹配概率矩阵,获得匹配的最终解。实验结果表明, 2种算法都具有较高的匹配精度。     

基于颜色和几何特征的图像特征点匹配算法

提出一种基于颜色和几何特征的图像特征点匹配算法。首先提取两幅图像特征点集邻域色调的局部累加直方图,然后结合图像特征点的几何特征构造亲近矩阵,再对亲近矩阵进行奇异值分解(SVD),利用分解的结果构造出一个反应特征点之间匹配程度的关系矩阵,最后根据关系矩阵实现两幅图像的特征点匹配。实验结果显示,这种图像特征点匹配算法对真实图像的平面旋转和立体旋转都具有较高的匹配精确度。     

Automatic feature matching in uncalibrated stereo vision through the use of color

One difficult problem in stereo vision is how to establish correspondence between features extracted from a pair of images. The difficulty is due to ambiguities or inconsistencies of available information on images. In this paper, we invetigate stereo correspondence problem in the framework of color stereo vision. We propose the use of a matching consistence (MC) constraint in RGB color space and the generalized epipolar geometry to develop an automatic feature matching algorithm.     

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。     

自动采集样本的图像颜色传递算法

图像的颜色传递是将一幅图像的颜色特征传递给另一幅图像,使目标图像具有与源图像相似的色彩。提出了一种自动采集样本的图像颜色传递方法,利用无监督学习聚类把目标图像和源图像分别分成一定数量的色彩相近的子块,通过计算各子块的纹理特征和亮度统计值,建立样本块之间的对应关系。从各子块中取出数据点密度较大的像素组成样本块。进而利用最佳匹配算法,查找目标样本块的像素在源图像对应样本块中的最佳匹配像素,将其颜色值传给目标像素。以目标图像中已完成颜色传递的样本块作为参考样本,使用纹理对比方式完成样本块以外其它像素的颜色传递。该自动采集样本算法既可用于彩色图像间颜色传递,也适用于灰度图像与彩色图像间颜色传递。在目标图像和源图像的各子块有明显纹理和亮度特征时,该算法可方便地用于批处理和视频中。     

Human appearance modeling for matching across video sequences

We present an appearance model for establishing correspondence between tracks of people which may be taken at different places, at different times or across different cameras. The appearance model is constructed by kernel density estimation. To incorporate structural information and to achieve invariance to motion and pose, besides color features, an additional feature of path-length is used. To achieve illumination invariance, two types of illumination insensitive color features are discussed: brightness color feature and RGB rank feature. The similarity between a test image and an appearance model is measured by the information gain or Kullback–Leibler distance. To thoroughly represent the information contained in a video sequence with as little data as possible, a key frame selection and matching scheme is proposed. Experimental results demonstrate the important role of the path-length feature in the appearance model and the effectiveness of the proposed appearance model and matching method.     

Image Retargeting Quality Assessment

Content‐aware image retargeting is a technique that can flexibly display images with different aspect ratios and simultaneously preserve salient regions in images. Recently many image retargeting techniques have been proposed. To compare image quality by different retargeting methods fast and reliably, an objective metric simulating the human vision system (HVS) is presented in this paper. Different from traditional objective assessment methods that work in bottom‐up manner (i.e., assembling pixel‐level features in a local‐to‐global way), in this paper we propose to use a reverse order (top‐down manner) that organizes image features from global to local viewpoints, leading to a new objective assessment metric for retargeted images. A scale‐space matching method is designed to facilitate extraction of global geometric structures from retargeted images. By traversing the scale space from coarse to fine levels, local pixel correspondence is also established. The objective assessment metric is then based on both global geometric structures and local pixel correspondence. To evaluate color images, CIE L*a*b* color space is utilized. Experimental results are obtained to measure the performance of objective assessments with the proposed metric. The results show good consistency between the proposed objective metric and subjective assessment by human observers.     

Unsupervised image matching based on manifold alignment

This paper challenges the issue of automatic matching between two image sets with similar intrinsic structures and different appearances, especially when there is no prior correspondence. An unsupervised manifold alignment framework is proposed to establish correspondence between data sets by a mapping function in the mutual embedding space. We introduce a local similarity metric based on parameterized distance curves to represent the connection of one point with the rest of the manifold. A small set of valid feature pairs can be found without manual interactions by matching the distance curve of one manifold with the curve cluster of the other manifold. To avoid potential confusions in image matching, we propose an extended affine transformation to solve the nonrigid alignment in the embedding space. The comparatively tight alignments and the structure preservation can be obtained simultaneously. The point pairs with the minimum distance after alignment are viewed as the matchings. We apply manifold alignment to image set matching problems. The correspondence between image sets of different poses, illuminations, and identities can be established effectively by our approach.     

基于协作Hopfield网络的迭代立体匹配算法

针对立体匹配算法中求解能量函数全局最小问题,提出一种基于协作Hopfield网络的迭代立体匹配算法.它采用两个具有相似结构的Hopfield神经网络协作求解匹配问题,两个网络的不同之处是匹配过程中所采用的基准图不同.然后根据左右一致性约束实现两个Hopfield网络之间的协作,从而避免落入局部最小.为加快收敛速度,该算法将视差图的最优搜索问题转换为二值神经网络的迭代收敛过程.利用局部匹配算法的结果预标记初始视差,以设定神经网络初始权重.并根据局部匹配算法中隐含的假定条件,提出了局部匹配算法视差结果的评估准则,以确定各像素的视差搜索范围,从而减少各次迭代过程中状态待确定的神经元个数.实验表明该方法在性能和收敛速度上都要优于传统的Boltzmann机方法.     

Inexact Bayesian point pattern matching for linear transformations

We introduce a novel Bayesian inexact point pattern matching model that assumes that a linear transformation relates the two sets of points. The matching problem is inexact due to the lack of one-to-one correspondence between the point sets and the presence of noise. The algorithm is itself inexact; we use variational Bayesian approximation to estimate the posterior distributions in the face of a problematic evidence term. The method turns out to be similar in structure to the iterative closest point algorithm.     

Measures Based on Fuzzy Similarity for Stereo Matching of Color Images

In this article, new local matching measures, based on fuzzy similarity, for stereo matching of color images are proposed and evaluated. By formulating the individual similarity between a pair of pixels as a conjunction of the similarities of the respective color components, the field programmable gate array implementation problem becomes more computationally tractable, since the word-length of the numbers carrying the similarity information can be reduced compared to standard techniques (Sum of absolute differences and Sum of squared differences). It is also shown that combining information about color and local image structure (horizontal gradient component) in the matching measure and using a multi-scale measure is advantageous compared to using standard measures, in terms of percentage of correct matches and MSE for the erroneous matches. However, these improvements come at the prize of more complex implementations. Since the techniques are window-based, they share the typical drawbacks associated with other techniques of this type. This is expected, since the focus is on developing new local matching measure without addressing issues like post-processing of the resulting disparity maps. The techniques have been tested on two stereo color image pairs.     

Stereo correspondence through feature grouping and maximal cliques

The authors propose a method for solving the stereo correspondence problem. The method consists of extracting local image structures and matching similar such structures between two images. Linear edge segments are extracted from both the left and right images. Each segment is characterized by its position and orientation in the image as well as its relationships with the nearby segments. A relational graph is thus built from each image. For each segment in one image as set of potential assignments is represented as a set of nodes in a correspondence graph. Arcs in the graph represent compatible assignments established on the basis of segment relationships. Stereo matching becomes equivalent to searching for sets of mutually compatible nodes in this graph. Sets are found by looking for maximal cliques. The maximal clique best suited to represent a stereo correspondence is selected using a benefit function. Numerous results obtained with this method are shown     

Unsupervised group matching with application to cross-lingual topic matching without alignment information

We propose a method for unsupervised group matching, which is the task of finding correspondence between groups across different domains without cross-domain similarity measurements or paired data. For example, the proposed method can find matching of topic categories in different languages without alignment information. The proposed method interprets a group as a probability distribution, which enables us to handle uncertainty in a limited amount of data, and to incorporate the high order information on groups. Groups are matched by maximizing the dependence between distributions, in which we use the Hilbert Schmidt independence criterion for measuring the dependence. By using kernel embedding which maps distributions into a reproducing kernel Hilbert space, we can calculate the dependence between distributions without density estimation. In the experiments, we demonstrate the effectiveness of the proposed method using synthetic and real data sets including an application to cross-lingual topic matching.     

一种新的形变指纹匹配方法

提出了一种基于细节点局部配准的形变指纹匹配方法。首先,结合细节点的纹理信息以及结构信息获取多个参照点;然后依据选取的多参照点实现模板指纹图像与输入指纹图像的全局配准从而获得指纹之间的公共区域;将公共区域内的细节点与它们最近的参照点聚类组合,形成多个分组,并将各分组内的细节点以对应的参照点为极点转化到极坐标系下建立指纹的局部对应性;最后,采用界限盒约束条件实现指纹匹配。实验结果表明,基于局部配准的指纹匹配方法对形变指纹匹配具有较好的鲁棒性,能较大提升指纹的识别性能。     

非参量变换在彩色图像立体匹配中的应用研究

随着彩色摄像机广泛使用,对彩色图像的视差匹配研究越来越普遍。研究显示,使用彩色图像匹配比灰度图像匹配精确度增加20％～25％。根据非参量变换原理,提出了基于非参量变换的彩色图像立体匹配算法,将Rank变换应用到彩色图像的区域匹配方法中,并采用census变换后的汉明码距离作为新的约束条件。实验表明提高了原来方法的抗噪性和匹配准确性。     

Statistical Approaches to Feature-Based Object Recognition

This paper examines statistical approaches to model-based object recognition.Evidence is presented indicating that, in some domains, normal (Gaussian) distributions are more accurate than uniform distributions for modeling feature fluctuations. This motivates the development of new maximum-likelihood and MAP recognition formulations which are based on normal feature models. These formulations lead to an expression for the posterior probability of the pose and correspondences given an image.Several avenues are explored for specifying a recognition hypothesis. In the first approach, correspondences are included as a part of the hypotheses. Search for solutions may be ordered as a combinatorial search in correspondence space, or as a search over pose space, where the same criterion can equivalently be viewed as a robust variant of chamfer matching. In the second approach, correspondences are not viewed as being a part of the hypotheses. This leads to a criterion that is a smooth function of pose that is amenable to local search by continuous optimization methods. The criteria is also suitable for optimization via the Expectation-Maximization (EM) algorithm, which alternates between pose refinement and re-estimation of correspondence probabilities until convergence is obtained.Recognition experiments are described using the criteria with features derived from video images and from synthetic range images.