Near-duplicate document image matching: A graphical perspective

A near-duplicate document image matching approach characterized by a graphical perspective is proposed in this paper. Document images are represented by graphs whose nodes correspond to the objects in the images. Consequently, the image matching problem is then converted to graph matching. To deal with the instability of object segmentation, a multi-granularity object tree is constructed for a document image. Each level in the tree corresponds to one possible object segmentation, while different levels are characterized by various object granularities. Some graphs can be generated from the tree and the objects associated with each graph may be of different granularities. Two graphs with the maximum similarity are found from the multi-granularity object trees of the two near-duplicate document images which are to be matched. The encouraging experimental results have demonstrated the effectiveness of the proposed approach.     

Superparsing

This paper presents a simple and effective nonparametric approach to the problem of image parsing, or labeling image regions (in our case, superpixels produced by bottom-up segmentation) with their categories. This approach is based on lazy learning, and it can easily scale to datasets with tens of thousands of images and hundreds of labels. Given a test image, it first performs global scene-level matching against the training set, followed by superpixel-level matching and efficient Markov random field (MRF) optimization for incorporating neighborhood context. Our MRF setup can also compute a simultaneous labeling of image regions into semantic classes (e.g., tree, building, car) and geometric classes (sky, vertical, ground). Our system outperforms the state-of-the-art nonparametric method based on SIFT Flow on a dataset of 2,688 images and 33 labels. In addition, we report per-pixel rates on a larger dataset of 45,676 images and 232 labels. To our knowledge, this is the first complete evaluation of image parsing on a dataset of this size, and it establishes a new benchmark for the problem. Finally, we present an extension of our method to video sequences and report results on a video dataset with frames densely labeled at 1 Hz.     

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。     

基于云遗传算法的图像相关匹配

针对图像相关匹配计算量大的问题,提出基于云遗传算法的图像相关匹配方法。考虑到图像平均量的存在会增加匹配的难度,对传统归一化相关测度进行修正。为寻找最佳匹配点,将修正后的相关测度作为适应度函数,采用云遗传算法进行寻优。由于云遗传算法具有收敛速度快、局部寻优能力强和不易产生早熟现象等优点,新方法的匹配精度和速度都得到提高,且抗噪声能力强。仿真实验结果表明,新方法对无噪声和有噪声图像都能实现高精度匹配,在匹配精度和速度上优于基于自适应遗传算法的匹配方法。     

一种基于球面调和描述子的3维模型相似性比较算法

提出了一种基于球面调和描述子的3维模型相似性比较算法。首先,对3维模型进行一分为二的递归分解,然后对每次递归分解得到的3维模型顶点集合进行球面映射得到其球面图像,最后计算所有球面图像的球面调和描述子得到3维模型的特征二叉树。通过对3维模型特征二叉树进行相似性比较可以得到3维模型的相似性。实验结果表明,该算法不仅能较好地比较3维模型相似性,而且对坐标系旋转变换、模型噪声、网格简化和细分具有较好的鲁棒性。     

Structural matching of 2D electrophoresis gels using deformed graphs

2D electrophoresis is a well-known method for protein separation which is extremely useful in the field of proteomics. Each spot in the image represents a protein accumulation and the goal is to perform a differential analysis between pairs of images to study changes in protein content. It is thus necessary to register two images by finding spot correspondences. Although it may seem a simple task, generally, the manual processing of this kind of images is very cumbersome, especially when strong variations between corresponding sets of spots are expected (e.g. strong non-linear deformations and outliers). In order to solve this problem, this paper proposes a new quadratic assignment formulation together with a correspondence estimation algorithm based on graph matching which takes into account the structural information between the detected spots. Each image is represented by a graph and the task is to find a maximum common subgraph. Successful experimental results using real data are presented, including an extensive comparative performance evaluation with ground-truth data.     

一种基于RDF图的本体匹配方法

本体匹配是建立两个本体之间映射关系的过程,一个高效、严格的相似度计算方法是本体匹配的前提条件,为此提出了一种基于RDF图匹配的方法。该方法用RDF图表示本体,使本体间的匹配问题转化为RDF图的匹配问题,并利用匹配树表示匹配的状态,通过匹配树计算出两个本体中各实体之间的相似度,进而得到两个本体之间的映射关系。实验结果表明,该方法在查全率和查准率方面都有很好的表现。     

基于小波估计和直方图特征的LSB匹配分析

针对隐写分析中的难点——空域LSB匹配隐写进行检测和分析,描述LSB匹配加性隐写的特点,将匹配隐写建模为图像受到一定强度的脉冲噪声干扰,采用小波变换对退化的图像进行恢复作为载体的估计。对检测图像和恢复图像提取多个直方图特征比值作为特征向量,利用支持向量机对500幅高质量未压缩的自然图像组成的载体、载密图像库进行检测,结果证明该算法在低嵌入率下可获得较好的检测效果。     

光照鲁棒的抗模糊新组合不变矩图像匹配方法

基于特征的传统图像匹配方法对环境光照变化和模糊噪声适应性较差,针对该问题文中提出了一种新组合不变矩。在Hu不变矩的基础上,重新组合构造出三个新组合不变矩并将其应用到图像匹配中。实验结果表明,这种新组合不变矩具有很好的光照鲁棒性和模糊不变性,能够有效地解决受光照变化和模糊噪声共同影响造成的匹配率下降问题;在保证高精度的前提下,匹配效率大大提高。该不变矩作为图像特征的描述形式进行图像匹配是有效可行的,具有良好的参考价值。     

基于特征区域的图像自动配准

为了解决基于特征的图像配准中的特征点的定义和提取问题,提出了一种以特征区域替代特征点的定义和提取方法。该方法应用Moravec算子选择候选特征区域,使用具有旋转不变性的Zernike矩表征该区域的特性;采用二级匹配策略进行特征区域的匹配,即基于自组织映射神经网络的初始匹配及精细匹配;建立图像的配准框架并实现图像的配准。实验结果表明,该方法能有效地提取图像的特征点并能准确地进行特征点的匹配,整个配准过程完全自动进行。     

Nighttime face recognition at large standoff: Cross-distance and cross-spectral matching

Face recognition in surveillance systems is important for security applications, especially in nighttime scenarios when the subject is far away from the camera. However, due to the face image quality degradation caused by large camera standoff and low illuminance, nighttime face recognition at large standoff is challenging. In this paper, we report a system that is capable of collecting face images at large standoff in both daytime and nighttime, and present an augmented heterogeneous face recognition (AHFR) approach for cross-distance (e.g., 150 m probe vs. 1 m gallery) and cross-spectral (near-infrared probe vs. visible light gallery) face matching. We recover high-quality face images from degraded probe images by proposing an image restoration method based on Locally Linear Embedding (LLE). The restored face images are matched to the gallery by using a heterogeneous face matcher. Experimental results show that the proposed AHFR approach significantly outperforms the state-of-the-art methods for cross-spectral and cross-distance face matching.     

一种基于模板匹配的电路照片拼接方法

鉴于实际工作中,经常需要将多幅图象拼成一大幅大图象的问题,提出了一种针对显微照片的新拼接方法。该方法是应用模板匹配技术来实现图象的自动拼接,由于是通过利用图象信息来选择模板,从而为模板匹配的定位创造了条件,该方法分为模板选择、模板匹配和图象拼接3步。实际运用结果表明,该方法简单易行,在电路照片的拼接中,定位较准确,且拼接效果好。     

基于上下文的异构文档类型定义匹配

XML文档的模式匹配是许多应用中的重要步骤．提出一种匹配算法来发现不同DTD中对应的元素和属性，通过转化规则将DTD转化成一个有向图模型，先给出了两个DTD树之间的匹配算法，然后将其推广一般的DTD图匹配的情形．匹配算法中在考察DTD节点的相似性时，除考虑标签语义和基数约束之外，研究了上下文语义对节点相似性的影响．     

Automatic Panoramic Image Stitching using Invariant Features

This paper concerns the problem of fully automated panoramic image stitching. Though the 1D problem (single axis of rotation) is well studied, 2D or multi-row stitching is more difficult. Previous approaches have used human input or restrictions on the image sequence in order to establish matching images. In this work, we formulate stitching as a multi-image matching problem, and use invariant local features to find matches between all of the images. Because of this our method is insensitive to the ordering, orientation, scale and illumination of the input images. It is also insensitive to noise images that are not part of a panorama, and can recognise multiple panoramas in an unordered image dataset. In addition to providing more detail, this paper extends our previous work in the area (Brown and Lowe, 2003) by introducing gain compensation and automatic straightening steps.     

Interactive noise-controlled boundary image matching using the time-series moving average transform

In this paper we propose a time-series matching-based approach that provides the interactive boundary image matching with noise control for a large-scale image database. To achieve the noise reduction effect in boundary image matching, we exploit the moving average transform of time-series matching. We are motivated by a simple intuition that the moving average transform might reduce the noise of boundary images as well as that of time-series data. To confirm this intuition, we first propose a new notion of k-order image matching, which applies the moving average transform to boundary image matching. A boundary image can be represented as a sequence in the time-series domain, and our k-order image matching identifies similar boundary images in this time-series domain by comparing the k-moving average transformed sequences. We then propose an index-based method that efficiently performs k-order image matching on a large image database, and formally prove its correctness. We also formally analyze the relationship of orders and their matching results and present an interactive approach of controlling the noise reduction effect. Experimental results show that our k-order image matching exploits the noise reduction effect well, and our index-based method outperforms the sequential scan by one or two orders of magnitude. These results indicate that our k-order image matching and its index-based solution provide a very practical way of realizing the noise control boundary image matching. To our best knowledge, the proposed interactive approach for large-scale image databases is the first attempt to solve the noise control problem in the time-series domain rather than the image domain by exploiting the efficient time-series matching techniques. Thus, our approach can be widely used in removing other types of distortions in image matching areas.     

基于网格多密度的古建筑图像特征匹配方法

古建筑图像三维重建中图像特征可靠匹配是影响重建效果的一个关键问题.为提高古建筑图像特征的匹配性能,提出了一种基于网格多密度聚类的特征匹配方法.该方法首先采用SIFT算子获取图像特征点;其次对图像进行网格划分,依据网格单元特征点密度确定图像锚单元、邻居单元、边界单元;然后依据局部区域密度相似性确定图像簇;最后对相似簇中的特征点依据最近邻距离比准则进行匹配.在中国古代建筑三维重建数据集和141幅山西晋祠古建筑图像上进行了实验,验证了算法的有效性.     

SAR与可见光图象匹配方法的研究

由于SAR和可见光成象传感器图象的物理特征不同,因此进行匹配难度很大。本文在深入分析了这两种传感器图象物理特征的基础上,提出了一种图象匹配的新方法。该方法运用形态学滤波方法去除SAR图象斑点噪声,利用图象的边缘特征进行匹配,并采用多分辨率分级搜索技术减少计算量,在对实验结果进行分析的基础上提出了搜索真实匹配位置的原则,大大提高了匹配的准确率。通过实验对SAR图象斑点噪声滤波和SAR与可见光图象匹配算法进行了验证,实验证明本算法的SAR图象去噪效果明显优于传统的Frost方法,图象匹配精度高,稳定性好。     

一种基于LTP特征的图像匹配方法*

为了解决可变光照条件下图像匹配困难这一问题,提出一种基于LTP（局部三值模式）特征的图像匹配方法。LTP是LBP（局部二值模式）的扩展,这种局部纹理描述算法较LBP更具有判别能力而且对于统一区域的噪声更不敏感。利用LTP对于图像旋转和光照变化都具有良好的鲁棒性,解决了在光照可变条件下流行的SIFT方法进行匹配的困难。通过对不同变换的图像进行匹配实验表明,该方法得到的匹配结果比LBP效果更好,鲁棒性更高,而在光照可变和噪声很大的情况下比流行的SIFT方法更实用。     

Matching hierarchical structures using association graphs

It is well-known that the problem of matching two relational structures can be posed as an equivalent problem of finding a maximal clique in a (derived) “association graph.” However, it is not clear how to apply this approach to computer vision problems where the graphs are hierarchically organized, i.e., are trees, since maximal cliques are not constrained to preserve the partial order. We provide a solution to the problem of matching two trees by constructing the association graph using the graph-theoretic concept of connectivity. We prove that, in the new formulation, there is a one-to-one correspondence between maximal cliques and maximal subtree isomorphisms. This allows us to cast the tree matching problem as an indefinite quadratic program using the Motzkin-Straus theorem, and we use “replicator” dynamical systems developed in theoretical biology to solve it. Such continuous solutions to discrete problems are attractive because they can motivate analog and biological implementations. The framework is also extended to the matching of attributed trees by using weighted association graphs. We illustrate the power of the approach by matching articulated and deformed shapes described by shock trees     

基于邻域灰度信息的Hausdorff距离图像匹配方法

针对可见光与红外图像由于成像机理不同引起的图像灰度值差异大、边缘轮廓不一致、传统基于灰度和基于特征的匹配方法匹配概率不高等问题,在分析了各种Hausdorff距离算法的前提下,引入可见光与红外图像的灰度信息,提出一种基于邻域灰度信息Hausdorff距离的图像匹配方法。该方法在计算图像边缘特征点相似性的基础上,增加了邻域归一化灰度方差计算,有效解决了由于边缘差异引起的Hausdorff距离算法对可见光/红外图像匹配概率不高的问题。经可见光与红外图像匹配的仿真实验表明,在各种条件下,该算法与传统Hausdorff距离算法相比,有效提高了在不同光照下图像的匹配效率以及对噪声的抗干扰性能。