Affine-invariant B-spline moments for curve matching

The article deals with the problem of matching and recognizing planar curves that are modeled by B-splines, independently of possible affine transformations to which the original curve has been subjected (for example, rotation, translation, scaling, orthographic, and semiperspective projections), and possible occlusion. It presents a fast algorithm for estimating the B-spline control points that is robust to nonuniform sampling, noise, and local deformations. Curve matching is achieved by using a similarity measure based on the B-spline knot points introduced by Cohen et al. (1991). This method, however, can neither handle the affine transformation between the curves nor the occlusion. Solutions to these two problems are presented through the use of a new class of weighted B-spline curve moments that are well defined for both open and closed curves. The method has been applied to classifying affine-transformed aircraft silhouettes, and appears to perform well.     

Registering coronal histological 2-D sections of a rat brain with coronal sections of a 3-D brain atlas using geometric curve invariants and B-spline representation

A new approach is proposed for registering a set of histological coronal two-dimensional images of a rat brain sectional material with coronal sections of a three-dimensional brain atlas, an intrinsic step and a significant challenge to current efforts in brain mapping and multimodal fusion of experimental data. The alignment problem is based on matching external contours of the brain sections, and operates in the presence of tissue distortion and tears which are routinely encountered, and possible scale, rotation, and shear changes (the affine and weak perspective groups). It is based on a novel set of local absolute affine invariants derived from the set of ordered inflection points on the external contour represented by a cubic B-spline curve. The inflection points are local intrinsic geometric features, which are preserved under both the affine and the weak perspective transformations. The invariants are constructed from the sequence of area patches bounded by the contour and the line connecting two consecutive inflection points, and hence do make direct use of the area (volume) invariance property associated with the affine transformation. These local absolute invariants are very well suited to handle the tissue distortion and tears (occlusion problem).     

Estimation of two-dimensional affine transformations through polar curve matching and its application to image mosaicking and remote-sensing data registration.

This paper presents a new and effective method for estimating two-dimensional affine transformations and its application to image registration. The method is based on matching polar curves obtained from the radial projections of the image energies, defined as the squared magnitudes of their Fourier transforms. Such matching is formulated as a simple minimization problem whose optimal solution is found with the Levenberg-Marquardt algorithm. The analysis of affine transformations in the frequency domain exploits the well-known property whereby the translational displacement in this domain can be factored out and separately estimated through phase correlation after the four remaining degrees of freedom of the affine warping have been determined. Another important contribution of this paper, emphasized through one example of image mosaicking and one example of remote sensing image registration, consists in showing that affine motion can be accurately estimated by applying our algorithm to the shapes of macrofeatures extracted from the images to register. The excellent performance of the algorithm is also shown through a synthetic example of motion estimation and its comparison with another standard registration technique.     

Elastic registration of biological images using vector-spline regularization

We present an elastic registration algorithm for the alignment of biological images. Our method combines and extends some of the best techniques available in the context of medical imaging. We express the deformation field as a B-spline model, which allows us to deal with a rich variety of deformations. We solve the registration problem by minimizing a pixelwise mean-square distance measure between the target image and the warped source. The problem is further constrained by way of a vector-spline regularization which provides some control over two independent quantities that are intrinsic to the deformation: its divergence, and its curl. Our algorithm is also able to handle soft landmark constraints, which is particularly useful when parts of the images contain very little information or when its repartition is uneven. We provide an optimal analytical solution in the case when only landmarks and smoothness considerations are taken into account. We have applied our approach to perform the elastic registration of images such as electrophoretic gels and fly embryos. The validation of the results by experts has been favorable in all cases.     

Medical image registration using mutual information

Analysis of multispectral or multitemporal images requires proper geometric alignment of the images to compare corresponding regions in each image volume. Retrospective three-dimensional alignment or registration of multimodal medical images based on features intrinsic to the image data itself is complicated by their different photometric properties, by the complexity of the anatomical objects in the scene and by the large variety of clinical applications in which registration is involved. While the accuracy of registration approaches based on matching of anatomical landmarks or object surfaces suffers from segmentation errors, voxel-based approaches consider all voxels in the image without the need for segmentation. The recent introduction of the criterion of maximization of mutual information, a basic concept from information theory, has proven to be a breakthrough in the field. While solutions for intrapatient affine registration based on this concept are already commercially available, current research in the field focuses on interpatient nonrigid matching.     

一种应用于图像配准中的点特征匹配算法

点特征匹配在机器视觉、图像配准等领域中有着重要的应用.针对空间存在较大仿射几何差异的图像中的点特征匹配问题,提出了一种利用马氏距离仿射不变性进行约束的松弛匹配算法,并将该算法应用于遥感图像配准中.实验结果表明,算法可以很好的完成点特征匹配,匹配点对数量充足且具备很高的正确率,从而可以保证图像配准的精度.     

A new image registration method for grey images

The proposed algorithm relies on a group of new formulas for calculating tangent slope so as to address angle feature of edge curves of image. It can utilize tangent angle features to estimate automatically and fully the rotation parameters of geometric transform and enable rough matching of images with huge rotation difference. After angle compensation, it can search for matching point sets by correlation criterion, then calculate parameters of affine transform, enable higher-precision emendation of rotation and transferring. Finally, it fulfills precise matching for images with relax-tense iteration method. Compared with the registration approach based on wavelet direction-angle features, the matching algorithm with tangent feature of image edge is more robust and realizes precise registration of various images. Furthermore, it is also helpful in graphics matching.     

基于曲线拟合的干涉合成孔径雷达(InSAR)复图像自动配准算法

由于乘性噪声和相干斑的存在,干涉合成孔径雷达(InSAR) 复图像对的配准算法比传统的图像配准算法更困难。该文提出了一种基于B-样条曲线拟合和匹配的InSAR复图像对的自动配准算法,能够有效地配准InSAR复图像对。在利用最小二乘法建立配准参数后,应用两步法完成了亚像元级配准。实验结果表明,该算法具有较高的精度和稳健性。     

基于直线空间结构特征的图像匹配方法

直线匹配在图像配准、目标识别等领域有重要应用。提出利用直线空间结构特征进行匹配的方法,算法采用粗精两级匹配的思路,粗匹配阶段用四参数仿射变换模型对待匹配图像进行粗步校正,精匹配阶段利用反映直线空间结构分布特征的直线交点方位角直方图,把直线匹配映射成两个点集的匹配,结合随机抽样共识(RANSAC)算法和参数迭代一致性方法剔除错配点对,实现图像的自动匹配。实验结果表明,本文方法能够取得较高的匹配精度。     

融合SIFT的B样条红外图像非刚性配准

手持式广角镜头红外热像仪所拍摄的不同时刻红外图像具有刚性形变和非刚性形变,传统图像配准算法很难同时矫正刚性形变与非刚性形变,针对该问题,提出一种融合SIFT的B样条配准算法。首先在待配准图像中建立控制网格,其次运用SIFT算法寻找待配准与基准图像间的匹配点对,剔除错误匹配点对并计算出待配准图像与基准图像间的刚性变换参数,接着对控制点进行刚性变换,最后以局部强度和为测度函数,运用B样条非刚性配准算法对广角镜头引起图像的非线性进行矫正。对比实验结果表明,本文算法具有很高配准精度,能够满足实际工程精度要求。     

基于SLER检测的红外与可见光图像自动配准

针对可见光图像和红外图像配准问题,提出了一种新的自动配准方法.该算法通过同级极值区域检测子在灰度梯度图像上提取仿射协变区域.然后利用超图匹配算法确定匹配点对实现图像配准.该方法尤其适合于红外图像的质量或者边缘比对应的可见图像质量或边缘差情况下的异模配准.对一些具有挑战性的图像对进行试验,实验结果表明我们提出的方法比其他方法获得了更好的性能.     

Automatic matching of homologous histological sections

The role of neuroanatomical atlases is undergoing a significant redefinition as digital atlases become available. These have the potential to serve as more than passive guides and to hold the role of directing segmentation and multimodal fusion of experimental data. Key elements needed to support these new tasks are registration algorithms. For images derived from histological procedures, the need is for techniques to map the two-dimensional (2-D) images of the sectional material into the reference atlas which may be a full three-dimensional (3-D) data set or one consisting of a series of 2-D images. A variety of 2-D-2-D registration methods are available to align experimental images with the atlas once the corresponding plane of section through the atlas has been identified. Methods to automate the identification of the homologous plane, however, have not been previously reported. Here, the authors use the external section contour to drive the identification and registration procedure. For this purpose, the authors model the contours by B-splines because of their attractive properties the most important of which are: (1) smoothness and continuity; (2) local controllability which implies that local changes in shape are confined to the B-spline parameters local to that change; (3) shape invariance under affine transformation, which means that the affine transformed curve is still a B-spline whose control points are related to the object control points through the transformation. The authors present a fast algorithm for estimating the control points of the B-spline which is robust to nonuniform sampling, noise, and local deformations. Curve matching is achieved by using a similarity measure that depends directly on the parameters of the B-spline. Performance tests are reported using histological material from rat brains     

Image registration based on lifting process: an application to digital subtraction radiography.

In this paper, a digital subtraction radiology scheme is presented based on a new method for the automatic registration of dental radiographs acquired with or without rigorous a priori standardization. The scheme is comprised of an automatic registration method and a subtraction process. The proposed registration method can be considered as an object-based registration method without imposing the prerequisite of image segmentation in order to detect the boundary of the objects of interest or the automatic detection of matching landmarks. This is achieved by augmenting the dimensionality of the problem from two-dimensional gray-level matching to three-dimensional surface matching using the process of lifting in combination with a surface-matching technique. The pseudo three-dimensional affine transformation that matches the lifted images incorporates advantageous characteristics including spatial alignment of the surfaces, anisotropic correction of brightness/contrast differences, and stable convergence of the similarity function to its optimal value. The performance of the proposed automatic registration method is assessed against a manual method based on the projective transformation. The qualitative and quantitative assessments of the experiments have shown advantageous performance of the proposed automatic registration method against the manual one. Finally, the proposed registration method has been further improved in terms of execution time by the implementation of a surface decimation process.     

基于兴趣点检测和仿射变换模型的序列图像拼接

针对序列图像的拼接问题,提出了一种基于兴趣点检测和仿射变换模型的图像配准算法,利用Harris算子检测兴趣点,并引入灰度交叉匹配和兴趣点坐标离散度两级约束策略确定同名兴趣点,通过在图像间建立仿射变换模型实现图像的配准;提出了一种自适应加权归一化函数对拼接图像进行处理,消除由于图像间灰度差异而导致的拼接缝。实验结果表明算法具有较高的配准精度和良好的像素融合效果。     

一种改进的快速鲁棒性特征匹配算法

针对光照变化、噪声、局部遮挡等在图像配准技术中对配准精度有重要影响,提出了一种在多尺度空间下点预测快速鲁棒性不变特征的匹配算法。针对在探测对图像的尺度、旋转,仿射具有不变性的斑状特征极值点过程中计算复杂度较高的问题,提出一种特征点预测方法降低了描述子提取的复杂度,增强了对外部环境光照变化、噪声以及局部遮挡的适应能力;并在KD(KD Tree)树基础上,提出了一种动态平衡KD树(DBKD-Tree)快速搜索匹配算法,有效克服了KD树可能存在的病态划分,采用条件约束最邻近搜索,提升匹配效率,实现特征点高精度匹配。通过对在不同光照条件、噪声环境的仿射变换图像特征匹配测试,在加入20%的高斯噪声后,均能100%地完成重复特征检测,达到亚像素定位精度,误配率降低为零。     

Image registration and object recognition using affine invariantsand convex hulls

This paper is concerned with the problem of feature point registration and scene recognition from images under weak perspective transformations which are well approximated by affine transformations and under possible occlusion and/or appearance of new objects. It presents a set of local absolute affine invariants derived from the convex hull of scattered feature points (e.g., fiducial or marking points, corner points, inflection points, etc.) extracted from the image. The affine invariants are constructed from the areas of the triangles formed by connecting three vertices among a set of four consecutive vertices (quadruplets) of the convex hull, and hence do make direct use of the area invariance property associated with the affine transformation. Because they are locally constructed, they are very well suited to handle the occlusion and/or appearance of new objects. These invariants are used to establish the correspondences between the convex hull vertices of a test image with a reference image in order to undo the affine transformation between them. A point matching approach for recognition follows this. The time complexity for registering L feature points on the test image with N feature points of the reference image is of order O(NxL). The method has been tested on real indoor and outdoor images and performs well.     

A maximum likelihood approach for image registration using control point and intensity

Registration of multidate or multisensor images is an essential process in many image processing applications including remote sensing, medical image analysis, and computer vision. Control point (CP) and intensity are the two basic features used separately for image registration in the literature. In this paper, an exact maximum likelihood (EML) registration method, which combines both CP and intensity, is proposed for image alignment. The EML registration method maximizes the likelihood function based CP and intensity to estimate the registration parameters, including affine transformation and CP coordinates. The explicit formulas of the Cramer-Rao bound (CRB) are also derived for the proposed EML and conventional image registration algorithms. The performances of these image registration techniques are evaluated with the CRBs.     

基于特征点对齐度的图像配准方法

该文提出了一个新的匹配准则特征点对齐度准则,并在此基础上提出了一种基于特征点对齐度准则的图像配准方法。首先,利用小波多尺度积提取出图像的特征点及其角度信息,再利用该文提出的特征点对齐度准则计算所有特征点对之间的对齐度,从而得到匹配点对。实验结果证明了该方法的精确性和有效性。     

基于小波变换和对齐度准则的图像配准方法

图像配准是多传感器图像融合研究中的一项关键技术.由于基于特征的图像配准方法存在特征提取的多样性,对提取图像特征的鲁棒性和精确性都有很高的要求.文中在适用性很强的对齐度准则的基础上,提出了基于小波变换和对齐度准则相结合的图像配准方法.首先,分别利用小波变换模极大值和小波多尺度积提取出图像的边缘及特征点,再利用对齐度准则计算所有特征点对之间的对齐度,从而得到匹配点对.实验结果表明,该方法具有较强的适用性、抗噪性,精确性和有效性.