基于角仿射不变的特征匹配

同一场景的不同图像匹配是计算机视觉中的一个基本问题，在诸如三维重建、对象识别和分类、图像配准和相机自校正等应用中，特征匹配都是一个关键步骤。为解决三维场景重建问题，通过改进目前特征匹配的不足，提出了一种基于角仿射不变的特征匹配算法。该方法是使用角作为图像匹配选取的特征，通过仿射不变处理来消除图像缩放、扭曲、旋转和平移产生的影响，实验表明，该算法具有良好的匹配性能，可以对差异较大的图像对进行特征匹配。     

基于分块和仿射不变性的SIFT图像匹配算法

针对传统SIFT算法在匹配时出现实时性差、匹配量低以及RANSANC算法在剔除SIFT误匹配对时误匹配率高的问题,提出一种基于距离相对性的分块匹配算法和基于仿射不变性的误匹配对剔除算法。首先利用传统SIFT算法提取图像中的特征点;然后采用基于距离相对性的分块匹配算法进行特征匹配得到初始匹配对;由于初始匹配对中存在误匹配,接下来运用基于仿射不变性的误匹配对剔除算法来剔除误匹配对;最后,在不同图像变换下进行仿真实验。实验结果表明,算法在保持SIFT算法鲁棒性的基础上,能够得到更多匹配对,正确匹配率提高了10%左右,并且实时性也得到很大改善。     

一种具有仿射不变性的图像匹配算法

针对仿射扭曲图像对之间的匹配问题,传统算法对仿射变换的6个参数都进行归一化,在仿射畸变严重时匹配效果不佳,不具备完全的仿射不变性。为此,提出一种基于参数仿真的图像匹配算法。根据经纬度对摄像机坐标的角度参数进行仿真,利用Harris-Laplace算法提取特征点,使用尺度不变特征变换对其进行描述,由此仿真尺度参数,并归一化平移和旋转参数。实验结果表明,与MSER、Harris-Affine、Hessian-Affine算法相比,该算法的鲁棒性较强,能使所有参数都具有不变性,具有完全的仿射不变性。     

Matching Widely Separated Views Based on Affine Invariant Regions

Invariant regions are self-adaptive image patches that automatically deform with changing viewpoint as to keep on covering identical physical parts of a scene. Such regions can be extracted directly from a single image. They are then described by a set of invariant features, which makes it relatively easy to match them between views, even under wide baseline conditions. In this contribution, two methods to extract invariant regions are presented. The first one starts from corners and uses the nearby edges, while the second one is purely intensity-based. As a matter of fact, the goal is to build an opportunistic system that exploits several types of invariant regions as it sees fit. This yields more correspondences and a system that can deal with a wider range of images. To increase the robustness of the system, two semi-local constraints on combinations of region correspondences are derived (one geometric, the other photometric). They allow to test the consistency of correspondences and hence to reject falsely matched regions. Experiments on images of real-world scenes taken from substantially different viewpoints demonstrate the feasibility of the approach.     

Geometric Invariant Shape Representations Using Morphological Multiscale Analysis

In this paper, we present a new geometric invariant shape representation using morphological multiscale analysis. The geometric invariant is based on the area and perimeter evolution of the shape under the action of a morphological multiscale analysis. First, we present some theoretical results on the perimeter and area evolution across the scales of a shape. In the case of similarity transformations, the proposed geometric invariant is based on a scale-normalized evolution of the isoperimetric ratio of the shape. In the case of general affine geometric transformations the proposed geometric invariant is based on a scale-normalized evolution of the area. We present some numerical experiments to evaluate the performance of the proposed models. We present an application of this technique to the problem of shape classification on a real shape database and we study the well-posedness of the proposed models in the framework of viscosity solution theory.     

Efficient Invariant Representations

Invariant representations are frequently used in computer vision algorithms to eliminate the effect of an unknown transformation of the data. These representations, however, depend on the order in which the features are considered in the computations. We introduce the class of projective/permutation p²-invariants which are insensitive to the labeling of the feature set. A general method to compute the p²-invariant of a point set (or of its dual) in the n-dimensional projective space is given. The one-to-one mapping between n + 3 points and the components of their p²-invariant representation makes it possible to design correspondence algorithms with superior tolerance to positional errors. An algorithm for coplanar points in projective correspondence is described as an application, and its performance is investigated. The use of p²-invariants as an indexing tool in object recognition systems may also be of interest.     

二维点模式图像的仿射变换配准

提出了一种图像特征点配准算法．该算法为二维点模式图像仿射配准建立了新的求解模型,此模型对参考图像的特征点集和浮动图像的特征点集分别进行Whitening变换,将点集间的一般仿射变换问题转换为刚性变换问题;在对刚性变换求解时,采用平滑性好、局部极值较少的新的目标函数;并引入了形变程度分量,使该算法更能符合实际应用．结合文中提出的新的仿射不变量,目标函数只需在平分法的基础上加入随机因素便能快速求解．实验结果证明,该算法在处理特征点仿射配准问题上具有速度快、精度高的特点．     

入侵检测系统中高效的模式匹配算法

针对入侵检测系统模式匹配效率低的问题,提出一种高效的模式匹配算法.该算法通过对模式进行预处理记录模式的信息,然后对子节点进行递归比较,找到重复度最大的部分,提高模式匹配的效率;通过增加附加m个节点的匹配模式结构,降低模式匹配算法的时间与空间复杂度.理论分析表明,对于包含n个节点的主题树,提出的模式匹配算法的时间复杂度为O(nlog2n+mlog2m),空间复杂度为O(n+m).详细的实验以及与现有算法的比较表明,提出的模式匹配算法在时间、空间和匹配率性能上具有更高的效率.     

基于三角网下的仿射不变几何约束的图像匹配算法研究

针对当前图像匹配方法在进行图像匹配时,主要通过度量特征向量之间的距离来完成图像匹配,导致算法鲁棒性差、误配率较高及效率较低等不足,本文提出了基于三角网下的仿射不变几何约束的图像匹配算法。首先,在尺度空间上通过Hessian矩阵对特征点进行检测,利用子块的三角特征与对角特征对SUR机制进行改进,用以生成新的特征描述子,并通过定义阀值评估策略,对图像特征点匹配,从而生成了初始匹配点;然后,引入 三角网,对初始匹配点进行聚类,以获取匹配三角形,将三角形以外的无效特征点剔除。最后,引入仿射不变几何约束,对匹配三角形进行细化,通过细化的匹配三角形获取最终的匹配特征点,有效剔除误配点,进一步提高配准精度。仿真结果表明,与当前图像匹配算法相比,本文算法具有更好的鲁棒性,且其具有更佳的匹配精度与效率,有效剔除了误配点。     

一种用于点模式匹配的改进型谱方法

利用谱方法进行点模式匹配的主要问题是对点的位置噪声比较敏感。为了提高谱方法对噪声的鲁棒性,该文在表示矩阵的构建过程中采用高斯加权的近邻矩阵对要匹配的点模式进行描述,提出一种新的符号校正方法,利用点的属性信息对根据谱方法得到的匹配度量进行加权。仿真实验表明,在噪声情况下的点模式匹配应用中采用改进的谱方法可以获得较高的正确匹配率。     

一种高效的XMLQuery基本模式匹配算法

有效的支持结构连接是实现数据库系统XML文件查询的关键。结构连接是用来查找所有满足基本的结构关系的元素对,即指定XML树型结构文件元素对的关系(父亲-孩子和祖先-子孙的关系)。文中在分析常见的XMLQuery模式匹配算法(Stack-Tree连接算法)的基础上,提出一种改进的Stack-Tree连接算法将Stack-Tree-Desc算法和Stack-Tree-Anc算法统一;并且采用动态分配存储空间方法,比Stack-Tree-Anc大大节省了存储空间。最后给出了改进的Stack-Tree连接算法分析和试验结果。     

一种高效的XMLQuery基本模式匹配算法

有效的支持结构连接是实现数据库系统XML文件查询的关键。结构连接是用来查找所有满足基本的结构关系的元素对，即指定XML树型结构文件元素对的关系（父亲-孩子和祖先-子孙的关系）。文中在分析常见的XMLQuery模式匹配算法（Stack-Tree连接算法）的基础上，提出一种改进的Stack-Tree连接算法将Stack—Tree—Desc算法和Stack—Tree—Anc算法统一；并且采用动态分配存储空间方法，比Stack—Tree—Anc大大节省了存储空间。最后给出了改进的Stack—Tree连接算法分析和试验结果。     

Temporal Pattern Matching Using Fuzzy Templates

The identification of temporal patterns plays an important role in many medical diagnostic applications. Template systems that identify events and landmark points directly from time-series information have been shown to work well in various applications and in various forms. However, few such systems directly account for the uncertainty and vagueness often associated with medical decision-making. This paper describes a template system that uses fuzzy set theory to provide a consistent mechanism of accounting for uncertainty in the existence of events, as well as vagueness in their starting times and durations. Fuzzy set theory allows the creation of fuzzy templates from linguistic rules. The fuzzy template system that is introduced in this paper can accommodate multiple time signals, relative or absolute trends, and obviates the need to also design a regression formula for pattern matching (a requirement in non-fuzzy template systems)—the system automatically generates a normalised 'goodness of fit score. Our target application for the fuzzy template system is anaesthesia monitoring. Initial testing using both simulated and recorded patient data has been encouraging. Results are presented showing the diagnosis using various temporal relationships of a number of problems.     

An Efficient Framework for Multiple Subgraph Pattern Matching Models

With the popularity of storing large data graph in cloud, the emergence of subgraph pattern matching on a remote cloud has been inspired. Typically, subgraph pattern matching is defined in terms of subgraph isomorphism, which is an NP-complete problem and sometimes too strict to find useful matches in certain applications. And how to protect the privacy of data graphs in subgraph pattern matching without undermining matching results is an important concern. Thus, we propose a novel framework to achieve the privacy-preserving subgraph pattern matching in cloud. In order to protect the structural privacy in data graphs, we firstly develop a k-automorphism model based method. Additionally, we use a cost-model based label generalization method to protect label privacy in both data graphs and pattern graphs. During the generation of the k-automorphic graph, a large number of noise edges or vertices might be introduced to the original data graph. Thus, we use the outsourced graph, which is only a subset of a k-automorphic graph, to answer the subgraph pattern matching. The efficiency of the pattern matching process can be greatly improved in this way. Extensive experiments on real-world datasets demonstrate the high efficiency of our framework.

     

Affine Invariant Texture Segmentation and Shape from Texture by Variational Methods

We address the problem of texture segmentation by using a novel affine invariant model. The introduction of affine invariance as a requirement for texture analysis goes beyond what is known of the human performance and also beyond the psychophysical theories. We propose to compute texture features using affine invariant intrinsic neighborhoods and affine invariant intrinsic orientation matrices. We discuss several possibilities for the definition of the channels and give comparative experimental results where an affine invariant Mumford-Shah type energy functional is used to compute the multichannel affine invariant segmentation. We prove that the method is able to retrieve faithfully the texture regions and to recover the shape from texture information in images where several textures are present. The numerical algorithm is multiscale.     

一种简单实用的草图编辑手势识别方法

联机草绘设计作为一种自然、高效的人机交互方式，继承了传统纸笔草绘设计的优点，弥补了现有CAD软件在处理模糊、不精确信息方面的不足，并日益受到人们的关注。针对联机草绘图形的编辑修改，设计出一套简单、快捷的编辑手势．并采用模式匹配的方法对其进行识别。实验结果表明，提出的方法具有较高的识别效果，加快了概念草图的设计过程。     

一种简单实用的草图编辑手势识别方法

联机草绘设计作为一种自然、高效的人机交互方式,继承了传统纸笔草绘设计的优点,弥补了现有CAD软件在处理模糊、不精确信息方面的不足,并日益受到人们的关注。针对联机草绘图形的编辑修改,设计出一套简单、快捷的编辑手势,并采用模式匹配的方法对其进行识别。实验结果表明,提出的方法具有较高的识别效果,加快了概念草图的设计过程。     

无重叠条件严格模式匹配的高效求解算法

无重叠条件序列模式挖掘是一种间隙约束序列模式挖掘方法,与同类挖掘方法相比,该方法更容易发现有价值的频繁模式,其核心问题是计算给定模式在序列中的支持度或出现数,进而判定该模式的频繁性.而计算模式支持度问题实质是无重叠条件模式匹配.当前研究采用迭代搜索无重叠出现,然后剪枝无用结点的方式计算模式的支持度,其计算时间复杂度为O （m×m×n×W）,其中,m,n和W分别为模式长度、序列长度及最大间隙.为了进一步提高无重叠条件模式匹配计算速度,从而有效地降低无重叠条件序列模式挖掘时间,提出了一种高效的算法,该算法将模式匹配问题转换为一棵网树,然后从网树的最小树根结点出发,采用回溯策略迭代搜索最左孩子方式计算无重叠最小出现,在网树上剪枝该出现后,无需进一步查找并剪枝无效结点即可实现问题的求解.理论证明了该算法的完备性,并将该算法的时间复杂度降低为O （m×n×W）.在此基础上,继续指明该问题还存在另外3种相似的求解策略,分别是从最左叶子出发迭代查找最左双亲方式、从最右树根出发迭代查找最右孩子方式和从最右叶子出发迭代查找最右双亲方式.实验结果验证了该算法的性能,特别是在序列模式挖掘中,应用该方法的挖掘算法可以降低挖掘时间.     

基于参数化求和不变量与特征重整的形状匹配

从特征提取和特征匹配两方面考虑,提出了一种鲁棒的形状匹配方法。首先,基于求和不变量,设计了基于面积的形状参数化和归一化方法,提出了参数化求和不变量,该不变量基于形状局部描述且采用积分算子计算,具有较好的鲁棒性和仿射不变性。然后,为进一步提高形状匹配的鲁棒性,在特征匹配上,分析了参数化求和不变量的先验信息,设计了基于特征重整的匹配距离函数,并通过动态规划进行实现。仿真实验表明了所提方法的有效性。