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。     

对环境光照鲁棒的全景图拼接

提出了一种从光照变化的序列图象中拼接全景图的方法 .该方法首先将待拼接图象的重叠部分分解成水平集表示 ,并且定义一个形态学距离用于测量水平集之间的相似度 ,然后根据这个形态学距离 ,对其中一幅图象的每一个水平集都在另一幅图象的水平集中找到对应 ,从而得到一个单调转换函数 ,用于表示两幅待拼接图象水平集之间的映射 ,用这个转换函数调整其中一幅图象的对比度与另一幅图象相对应 ;最后 ,用基于灰度匹配的方法将两幅图象拼接 ,图象两两拼接后经全局误差校正即可得到一幅正确拼接的全景图 .该方法可以广泛应用于基于图象的绘制、图象处理等领域     

基于颜色分布相似性的图象内容检索

图象内容的检索需要确定图象之间的相似性。本文给出了一个基于颜色直方图相似性的匹配模型，然后描述了基于颜色直方图匹配方法的实现，讨论了这个方法在图象相似性上的度量性能。     

基于Hausdorff距离图象配准方法研究

图象配准是图象融合的一个重要步骤，为此提出了一种自动图象配准算法，该算法从两幅待配准的图象中分别抽取特征点，然后选用Hausdorff距离对两特征点集进行匹配，得到点集间的仿射变换，从而实现图象的自动配准，此算法以特征点而不是物体边缘计算仿射变换，大大降低了计算Hausdorff距离的运算量；同时，基于Hausdorff距离的图象匹配只需要点集之间的对应，而无须点与点的对应，因而可以使用于存在较大物体形变的情况，即完成两幅差异较大图象的配准，实验结果证明了算法的有效性。     

基于变形轮廓的医学图象匹配方法

艇变形轮廓线的方法实现医学图象匹配过程中的对应标记点问题,并用遗传算法进行最佳匹配结果的搜索。实验结果表明,该方法能有效地用于建立医学图谱和图象之间的一一对应关系,是对以往人工交互标注方法的重要改进。     

一种基于多尺度小波分解的图象镶嵌技术

为了获得一种具有艺术视觉效果的镶嵌图象,提出了一种基于多尺度小波分解的图象镶嵌技术,该技术首先对原始图象的各子块区域和图象库中的每一幅图象进行多尺度小波分解;然后逐层计算各图象小波分解系数的标准方差和它们之间的相似距离,并据此从图象库中选取与原始图象中的各子块区域最佳匹配的贴图,再将其镶嵌到原始图象中的对应区域;最后对贴图进行逐像素的颜色校正,使贴图的颜色与原始图象尽可能一致.同时,根据人眼观察某个区域时往往通过取整或将细节取平均来得到一个总体强度效果这一视觉特性,使最终的镶嵌图象具有在近处看到的是各个贴图的内容,而在远处观看则是原始图象的总体轮廓的视觉效果.利用计算机来自动地生成这种镶嵌图象的实验结果表明,该方法是有效的.     

一种基于边缘轮廓信息的多源遥感图象子象素匹配定位方法

提出了一种基于图象边缘轮廓信息的多源图象匹配定位方法,其目的是利用定位精度较高的高分辩率遥感图象对低分辨率图象实现子象素级的匹配定位,该方法有效地利用了多源遥感图象中共有的区域结构信息,将特征匹配和最小二乘影象匹配相结合,具有较好的普适性,且运算快速、抗噪性能好,采用该方法进行ＮＯＡＡＡＶＨＲＲ图象和Ｌａｎｄｓａｔ ＴＭ图象、１：１００万数字地图的边缘图象匹配,并应用于ＮＯＡＡ ＡＶＨＲＲ图象的几     

基于环视图象的数字相机内参数自定标方法

描述了一种适用于IBR系统的数字相机内参数自定标方法。该方法基于跟踪机机旋转得到的图象系列的特征匹配点以,而不需要标定物。认定在相机旋转过程中,其光学中心是稳定不变的,也即图象中心是固定的,可以事先定标;但容许相机的焦距在各幅图象间有变化,利用真实图象序列进行了实验验证,表明该方法能鲁棒地估算相机内参数。     

一种新的指纹匹配方法

针对基于点模式匹配的指纹匹配算法速度较慢的现状，设计了一种新的指纹匹配方法，即利用纹线匹配技术来寻找基准点对的指纹匹配算法．该算法首先基于指纹纹线的相似程度寻找一对基准特征点；然后根据基准点对的坐标，计算两幅指纹图象(模板图象、待识图象)的相对平移和旋转参数，并将待识图象相对于模板图象进行图象姿势纠正；最后使用坐标匹配的方法统计两幅图象能够匹配的特征点数目．以实现两枚指纹的匹配．实验证明．该算法匹配速度很快，误识率低，准确性高，并具有图象旋转平移不变性．对面积适中的指纹图象，匹配结果可以满足在线应用的需要．该算法有望发展成为一种实用、有效的指纹匹配技术．     

图象拼接中伪匹配的判别和消解

图象拼接就是把边界部分重叠的多幅图象接成一幅完整图象,图象拼接中的困难之一是：当相邻两幅图象重叠区域缺乏显著特征,或者存在多个特征相似的部分,就会发生伪匹配而导致拼接失败。该文基于均方误差曲线和相邻空间约束关系,提出了一种伪匹配判别方法。实验表明,该方法可以有效的进行伪匹配的判别和消解。     

Image fusion enhancement of deformable human structures using a two-stage warping-deformable strategy: A content-based image retrieval consideration

In medical image registration and content-based image retrieval, the rigid transformation model is not adequate for anatomical structures that are elastic or deformable. For human structures such as abdomen, registration would involve global features such as abdominal wall as well as local target organs such as liver or spleen. A general non-rigid registration may not be sufficient to produce image matching of both global and local structures. In this study, a warping-deformable model is proposed to register images of such structures. This model uses a two-stage strategy for image registration of abdomen. In the first stage, the global-deformable transformation is used to register the global wall. The warping-transformation is used in second stage to register the liver. There is a good match of images using the proposed method (mean similarity index = 0.73545).The image matching correlation coefficients calculated from eight pairs of CT and MR images of abdomen indicates that the warping-deformable transformation gives better matching of images than those without transformation (p < 0.001, paired t-test). This study has established a model for image registration of deformable structures. This is particularly important for data mining of image content retrieval for structures which are non-rigid. The result obtained is very promising but further clinical evaluation is needed     

Matching point features under small nonrigid motion

This paper describes a method for matching point features between images of objects that have undergone small nonrigid motion. Feature points are assumed to be available and, given a properly extracted set of feature points, a robust matching is established under the condition that the local nonrigid motion of each point is restricted to a circle of radius δ, where δ is not too large. This is in contrast to other techniques for point matching which assume either rigid motion or nonrigid motion of a known kind. The point matching problem is viewed in terms of weighted bipartite graph matching. In order to account for the possibility that the feature selector can be imprecise, we incorporate a greedy matching strategy with the weighted graph matching algorithm. Our algorithm is robust and insensitive to noise and missing features. The resulting matching can be used with image warping or other techniques for nonrigid motion analysis, image subtraction, etc. We present our experimental results on sequences of mammograms, images of a deformable clay object and satellite cloud images. In the first two cases we provide quantitative comparison with known ground truth.     

Flexible syntactic matching of curves and its application toautomatic hierarchical classification of silhouettes

Curve matching is one instance of the fundamental correspondence problem. Our flexible algorithm is designed to match curves under substantial deformations and arbitrary large scaling and rigid transformations. A syntactic representation is constructed for both curves and an edit transformation which maps one curve to the other is found using dynamic programming. We present extensive experiments where we apply the algorithm to silhouette matching. In these experiments, we examine partial occlusion, viewpoint variation, articulation, and class matching (where silhouettes of similar objects are matched). Based on the qualitative syntactic matching, we define a dissimilarity measure and we compute it for every pair of images in a database of 121 images. We use this experiment to objectively evaluate our algorithm. First, we compare our results to those reported by others. Second, we use the dissimilarity values in order to organize the image database into shape categories. The veridical hierarchical organization stands as evidence to the quality of our matching and similarity estimation     

Spatial transformation and registration of brain images using elastically deformable models.

The development of algorithms for the spatial transformation and registration of tomographic brain images is a key issue in several clinical and basic science medical applications, including computer-aided neurosurgery, functional image analysis, and morphometrics. This paper describes a technique for the spatial transformation of brain images, which is based on elastically deformable models. A deformable surface algorithm is used to find a parametric representation of the outer cortical surface and then to define a map between corresponding cortical regions in two brain images. Based on the resulting map, a three-dimensional elastic warping transformation is then determined, which brings two images into register. This transformation models images as inhomogeneous elastic objects which are deformed into registration with each other by external force fields. The elastic properties of the images can vary from one region to the other, allowing more variable brain regions, such as the ventricles, to deform more freely than less variable ones. Finally, the framework of prestrained elasticity is used to model structural irregularities, and in particular the ventricular expansion occurring with aging or diseases, and the growth of tumors. Performance measurements are obtained using magnetic resonance images.     

Physical Scale Keypoints: Matching and Registration for Combined Intensity/Range Images

We present a new framework for detecting, describing, and matching keypoints in combined range-intensity data, resulting in what we call physical scale keypoints. We first produce an image mesh by backprojecting associated 2D intensity images onto the 3D range data. We detect and describe keypoints on the image mesh using an analogue of the SIFT algorithm for images with two key modifications: the process is made insensitive to viewpoint and structural discontinuities using a novel bilinear filter, and a physical scale space is constructed that exploits the reliable range measurements. Keypoints are matched between scans only when their physical scales agree, avoiding many potential false matches. Finally, the matches are rank-ordered using a new quality measure and supplied to a registration algorithm that refines each match into a rigid transformation for the entire scan pair. We report experimental results on keypoint detection and matching and range scan registration and verification in a set of difficult real-world scan pairs, showing that the new physical scale keypoints are demonstrably better than a competing approach based on backprojected SIFT keypoints.     

Matching point features with ordered geometric, rigidity, anddisparity constraints

This correspondence presents a matching algorithm for obtaining feature point correspondences across images containing rigid objects undergoing different motions. First point features are detected using newly developed feature detectors. Then a variety of constraints are applied starting with simplest and following with more informed ones. First, an intensity-based matching algorithm is applied to the feature points to obtain unique point correspondences. This is followed by the application of a sequence of newly developed heuristic tests involving geometry, rigidity, and disparity. The geometric tests match two-dimensional geometrical relationships among the feature points, the rigidity test enforces the three dimensional rigidity of the object, and the disparity test ensures that no matched feature point in an image could be rematched with another feature, if reassigned another disparity value associated with another matched pair or an assumed match on the epipolar line. The computational complexity is proportional to the numbers of detected feature points in the two images. Experimental results with indoor and outdoor images are presented, which show that the algorithm yields only correct matches for scenes containing rigid objects     

Elastic Matching of Diffusion Tensor Images

In this paper, we discuss matching of magnetic resonance, diffusion tensor (DT) images of the human brain. Issues concerned with matching and transforming these complex images are discussed. In particular, we outline a method for preserving the intrinsic orientation of the data during nonrigid warps of the image and a number of similarity measures are proposed, based on the DT itself, on the DT deviatoric, and on indices derived from the DT. Each measure is used to drive an elastic matching algorithm applied to the task of registration of 3D images of the human brain. The performance of the various similarity measures is compared empirically by the use of several quality of match measures computed over a pair of matched images. Results indicate that the best matches are obtained from a Euclidean difference measure using the full DT.     

Fast deformable matching of 3D images over multiscale nested subspaces. Application to atlas-based MRI segmentation

This paper presents a fast method to perform dense deformable matching of 3D images, applied to the registration of inter-subject brain MR images. To recover the complex morphological variations in neuroanatomy, the registration method uses a hierarchy of 3D deformations fields that are estimated, by minimizing a global energy function over a sequence of nested subspaces. The resulting deformable matching method shows low sensitivity to local minima and is able to track large non-linear deformations, with moderate computational load. The performances of the approach are assessed both on simulated 3D transformations and on a real data base of 3D brain MR images from different individuals. An application of the deformable image matching method to 3D atlas-based image segmentation is presented. This atlas-based segmentation is used at Strasbourg Hospital, in daily clinical applications, in order to extract regions of interest from 3D MR images of patients suffering from epilepsy.     

基于联合图频谱特征分析的异源图像匹配方法

针对光线强度过高或过低情况下的异源图像匹配问题,提出一种基于联合图频谱特征分析的异源图像匹配方 法。首先,采用K 近邻法则计算可见光图像与红外图像中角点的结构关系并构建联合图;接着,基于拉普拉斯分解计算联合图 中邻接矩阵的特征值从而得到邻接矩阵的特征向量,并通过三维重构构建特征函数对;第三,提出一种基于SU SAN -M SER - SU R F最大稳定极值区域检测器,检测特征函数对的极值位置;最后,通过对最大稳定极值区域进行归一化后匹配,可以得到 异源图像的精确匹配结果。实验结果表明,提出的基于联合图频谱特征分析的匹配方法能够解决光强过高或过低情况下的异 源图像匹配问题并取得较优异的匹配率。