基于混合特征的非刚性点阵配准算法

提出一种基于混合特征的非刚性点阵配准算法.该算法包含了对应关系评估与空间变换更新两个相互交替的步骤.首先定义了两个特征描述法用于描述两个点阵之间的全局和局部几何结构特征差异,随后合并这两个特征描述法建立一个基于混合特征的能量优化方程.该能量优化方程可以利用线性分配技术进行求解,同时可以灵活地选择使用最小化全局结构特征差异或最小化局部结构特征差异来评估两个点阵之间的对应关系.为了增强前述两个步骤之间的协调性,我们利用能量权重调节在整个配准过程中控制能量优化从最小化局部结构特征差异逐步转变为最小化全局结构特征差异,同时控制用于空间变换的薄板样条函数（Thin plate spline）的更新从刚性变换逐步转变为非刚性变换.我们在二维轮廓配准、三维轮廓配准、序列图像配准和图像特征点配准下对本文算法进行了各项性能测试,同时也与当前8种流行算法进行了性能比较.本文算法展现了卓越的非刚性配准性能,并在大部分实验中超越了当前的相关算法.     

A robust global and local mixture distance based non-rigid point set registration

We present a robust global and local mixture distance (GLMD) based non-rigid point set registration method which consists of an alternating two-step process: correspondence estimation and transformation updating. We first define two distance features for measuring global and local structural differences between two point sets, respectively. The two distances are then combined to form a GLMD based cost matrix which provides a flexible way to estimate correspondences by minimizing global or local structural differences using a linear assignment solution. To improve the correspondence estimation and enhance the interaction between the two steps, an annealing scheme is designed to gradually change the cost minimization from local to global and the thin plate spline transformation from rigid to non-rigid during registration. We test the performance of our method in contour registration, sequence images and real images, and compare with six state-of-the-art methods where our method shows the best alignments in most scenarios.     

Fingerprint image mosaicking by recursive ridge mapping.

To obtain a large fingerprint image from several small partial images, mosaicking of fingerprint images has been recently researched. However, existing approaches cannot provide accurate transformations for mosaics when it comes to aligning images because of the plastic distortion that may occur due to the nonuniform contact between a finger and a sensor or the deficiency of the correspondences in the images. In this paper, we propose a new scheme for mosaicking fingerprint images, which iteratively matches ridges to overcome the deficiency of the correspondences and compensates for the amount of plastic distortion between two partial images by using a thin-plate spline model. The proposed method also effectively eliminates erroneous correspondences and decides how well the transformation is estimated by calculating the registration error with a normalized distance map. The proposed method consists of three phases: feature extraction, transform estimation, and mosaicking. Transform is initially estimated with matched minutia and the ridges attached to them. Unpaired ridges in the overlapping area between two images are iteratively matched by minimizing the registration error, which consists of the ridge matching error and the inverse consistency error. During the estimation, erroneous correspondences are eliminated by considering the geometric relationship between the correspondences and checking if the registration error is minimized or not. In our experiments, the proposed method was compared with three existing methods in terms of registration accuracy, image quality, minutia extraction rate, processing time, reject to fuse rate, and verification performance. The average registration error of the proposed method was less than three pixels, and the maximum error was not more than seven pixels. In a verification test, the equal error rate was reduced from 10% to 2.7% when five images were combined by our proposed method. The proposed method was superior to other compared methods in terms of registration accuracy, image quality, minutia extraction rate, and verification.     

Robust point correspondence applied to two- and three-dimensionalimage registration

Accurate and robust correspondence calculations are very important in many medical and biological applications. Often, the correspondence calculation forms part of a rigid registration algorithm, but accurate correspondences are especially important for elastic registration algorithms and for quantifying changes over time. In this paper, a new correspondence calculation algorithm, CSM (correspondence by sensitivity to movement), is described. A robust corresponding point is calculated by determining the sensitivity of a correspondence to movement of the point being matched. If the correspondence is reliable, a perturbation in the position of this point should not result in a large movement of the correspondence. A measure of reliability is also calculated. This correspondence calculation method is independent of the registration transformation and has been incorporated into both a 2D elastic registration algorithm for warping serial sections and a 3D rigid registration algorithm for registering pre and postoperative facial range scans. These applications use different methods for calculating the registration transformation and accurate rigid and elastic alignment of images has been achieved with the CSM method. It is expected that this method will be applicable to many different applications and that good results would be achieved if it were to be inserted into other methods for calculating a registration transformation from correspondences     

Frame-rate spatial referencing based on invariant indexing and alignment with application to online retinal image registration

This paper describes an algorithm to continually and accurately estimate the absolute location of a diagnostic or surgical tool (such as a laser) pointed at the human retina, from a series of image frames. We treat the problem as a registration problem using diagnostic images to build a spatial map of the retina and then registering each online image against this map. Since the image location where the laser strikes the retina is easily found, this registration determines the position of the laser in the global coordinate system defined by the spatial map. For each online image, the algorithm computes similarity invariants, locally valid despite the curved nature of the retina, from constellations of vascular landmarks. These are detected using a high-speed algorithm that iteratively traces the blood vessel structure. Invariant indexing establishes initial correspondences between landmarks from the online image and landmarks stored in the spatial map. Robust alignment and verification steps extend the similarity transformation computed from these initial correspondences to a global, high-order transformation. In initial experimentation, the method has achieved 100 percent success on 1024 /spl times/ 1024 retina images. With a version of the tracing algorithm optimized for speed on 512 /spl times/ 512 images, the computation time is only 51 milliseconds per image on a 900MHz PentiumIII processor and a 97 percent success rate is achieved. The median registration error in either case is about 1 pixel.     

一种应用于大角度变换点云的配准方法

针对传统配准法不能很好解决大角度变换点云的配准这一问题,提出一种基于精 确对应特征点对及其 K 邻域点云的配准方法。首先分别计算两组点云的 FPFH 值,根据特征值 建立点云间的对应关系;然后通过 RANSAC 滤除其中错误的匹配点对,得到相对精确的特征点 对集合;之后通过 KD-tree 搜索的方式分别找出特征点对 R 半径邻域内的点,应用 ICP 算法得 到两部分点云的最优收敛;最后将计算得到的相对位置关系应用到原始点云上得到配准结果。 通过对斯坦福大学点云库中 Dragon、Happy Buddha 模型以及 Kinect 采集的石膏像数据进行配 准和比较,实验表明该方法能够有效解决大角度变换点云的配准问题,是一种具有高精度和高 鲁棒性的三维点云配准方法。     

Feature matching based on unsupervised manifold alignment

Feature-based methods for image registration frequently encounter the correspondence problem. In this paper, we formulate feature-based image registration as a manifold alignment problem, and present a novel matching method for finding the correspondences among different images containing the same object. Different from the semi-supervised manifold alignment, our methods map the data sets to the underlying common manifold without using correspondence information. An iterative multiplicative updating algorithm is proposed to optimize the objective, and its convergence is guaranteed theoretically. The proposed approach has been tested for matching accuracy, and robustness to outliers. Its performance on synthetic and real images is compared with the state-of-the-art reference algorithms.     

Robust dense registration of partial nonrigid shapes

This paper presents a complete and robust solution for dense registration of partial nonrigid shapes. Its novel contributions are founded upon the newly proposed heat kernel coordinates (HKCs) that can accurately position points on the shape, and the priority-vicinity search that ensures geometric compatibility during the registration. HKCs index points by computing heat kernels from multiple sources, and their magnitudes serve as priorities of queuing points in registration. We start with shape features as the sources of heat kernels via feature detection and matching. Following the priority order of HKCs, the dense registration is progressively propagated from feature sources to all points. Our method has a superior indexing ability that can produce dense correspondences with fewer flips. The diffusion nature of HKCs, which can be interpreted as a random walk on a manifold, makes our method robust to noise and small holes avoiding surface surgery and repair. Our method searches correspondence only in a small vicinity of registered points, which significantly improves the time performance. Through comprehensive experiments, our new method has demonstrated its technical soundness and robustness by generating highly compatible dense correspondences.     

6D scan registration using depth-interpolated local image features

This paper describes a novel registration approach that is based on a combination of visual and 3D range information. To identify correspondences, local visual features obtained from images of a standard color camera are compared and the depth of matching features (and their position covariance) is determined from the range measurements of a 3D laser scanner. The matched depth-interpolated image features allow one to apply registration with known correspondences. We compare several ICP variants in this paper and suggest an extension that considers the spatial distance between matching features to eliminate false correspondences. Experimental results are presented in both outdoor and indoor environments. In addition to pair-wise registration, we also propose a global registration method that registers all scan poses simultaneously.     

Multiview registration of 3D scenes by minimizing error between coordinate frames

This paper addresses the problem of large-scale multiview registration of range images captured from unknown viewing directions. To reduce the computational burden, we separate the local problem of pairwise registration on neighboring views from the global problem of distribution of accumulated errors. We define the global problem as an optimization over the graph of neighboring views, and we show how the graph can be decomposed into a set of cycles such that the optimal transformation parameters for each cycle can be solved in closed form. We then describe an iterative procedure that can be used to integrate the solutions for the set of cycles across the graph of views. This method for error distribution does not require point correspondences between views, and can be used to integrate any method of pairwise registration or robot odometry.     

Automatic Range Image Registration in the Markov Chain

In this paper, a novel entropy that can describe both long and short-tailed probability distributions of constituents of a thermodynamic system out of its thermodynamic limit is first derived from the Lyapunov function for a Markov chain. We then maximize this entropy for the estimation of the probabilities of possible correspondences established using the traditional closest point criterion between two overlapping range images. When we change our viewpoint to look carefully at the minimum solution to the probability estimate of the correspondences, the iterative range image registration process can also be modeled as a Markov chain in which lessons from past experience in estimating those probabilities are learned. To impose the two-way constraint, outliers are explicitly modeled due to the almost ubiquitous occurrence of occlusion, appearance, and disappearance of points in either image. The estimated probabilities of the correspondences are finally embedded into the powerful mean field annealing scheme for global optimization, leading the camera motion parameters to be estimated in the weighted least-squares sense. A comparative study using real images shows that the proposed algorithm usually outperforms the state-of-the-art ICP variants and the latest genetic algorithm for automatic overlapping range image registration.     

Free form shape registration using the barrier method

This paper formalizes overlapping free form shape registration as a minimization problem, which minimizes a weighted sum of registration errors of tentative correspondences with the weights subject to boundary conditions. The tentative correspondences are established using the traditional closest point criterion. Then the powerful barrier method is employed to transform the constrained minimization problem to an unconstrained one. Setting the first order derivative of the unconstrained objective function to zero results in the weights being solved with a closed form solution. The weights are finally globally optimized using the deterministic annealing scheme. Outliers due to occlusion, appearance and disappearance of points in either free form shape are explicitly modelled using a constant. The camera motion parameters are updated in the weighted least squares sense. A comparative study based on both synthetic data and real images shows that the proposed algorithm is promising for the accurate and robust automatic registration of overlapping free form shapes.     

匹配与姿态估计的粒子群优化算法

点模式匹配问题是机器视觉与模式识别领域中一个基础问题,在目标识别、医学图像配准、遥感图像匹配、姿态估计等方面都得到广泛应用。提出一种在仿射变换下利用粒子群优化算法进行图像点模式下的匹配与姿态估计的方法。算法首先把点集匹配问题转化为解空间为仿射参数空间下的目标函数优化问题,然后运用粒子群算法对相应的变换参数进行搜索,获得问题最优解。本文贡献如下：1)给出一种仿射参数的初始估计方法,提高了后续算法搜索效率;2)引入阈值和次近点规则,改进了最近点匹配搜索方法,能较好地拒绝出格点（outliers）,并提高算法有效性;3)从两方面对PSO方法进行了改进,加强了原PSO的全局和局部搜索能力。实验结果表明,算法具有有效性和鲁棒性。     

A coarse-to-fine IP-driven registration for pose estimation from single ultrasound image

A fast registration making use of implicit polynomial (IP) models is helpful for the real-time pose estimation from single clinical free-hand Ultrasound (US) image, because it is superior in the areas such as robustness against image noise, fast registration without enquiring correspondences, and fast IP coefficient transformation. However it might lead to the lack of accuracy or failure registration.In this paper, we present a novel registration method based on a coarse-to-fine IP representation. The approach starts from a high-speed and reliable registration with a coarse (of low degree) IP model and stops when the desired accuracy is achieved by a fine (of high degree) IP model. Over the previous IP-to-point based methods our contributions are: (i) keeping the efficiency without requiring pair-wised correspondences, (ii) enhancing the robustness, and (iii) improving the accuracy. The experimental result demonstrates the good performance of our registration method and its capabilities of overcoming the limitations of unconstrained freehand ultrasound data, resulting in fast, robust and accurate registration.     

ICP registration using invariant features

Investigates the use of Euclidean invariant features in a generalization of iterative closest point (ICP) registration of range images. Pointwise correspondences are chosen as the closest point with respect to a weighted linear combination of positional and feature distances. It is shown that, under ideal noise-free conditions, correspondences formed using this distance function are correct more often than correspondences formed using the positional distance alone. In addition, monotonic convergence to at least a local minimum is shown to hold for this method. When noise is present, a method that automatically sets the optimal relative contribution of features and positions is described. This method trades off the error in feature values due to noise against the error in positions due to misalignment. Experimental results suggest that using invariant features decreases the probability of being trapped in a local minimum and may be an effective solution for difficult range image registration problems where the scene is very small compared to the model     

基于仿射变换模型的图象特征点集配准方法研究

图象配准是计算机视觉中目标识别的一种基本方法,其目的是在待识别图象中寻找与模型图象的最佳匹配.目前,对于图象间的变换为相似变换的情形已有闭合公式.本文则分别运用最小二乘和矩阵伪逆两种方法,对图象间的变换为仿射变换的情形进行了研究,并给出了简单的闭合公式.实验表明这种方法精确、稳定、受噪声影响小.     

基于射影变换模型的图像特征点集配准

图像配准是计算机视觉中目标识别的一种基本方法,其目的是在待识别图像中寻找与模型图像的最佳匹配。该文讨论以特征点表示的图像间的配准问题,利用矩阵分解理论推导出射影变换下特征点集配准的闭合公式,给出变换参数估计的算法,并用模拟数据和图像角点检测的真实数据加以验证。实验表明该方法精确、稳定、受噪声影响小。     

3D fingerprint reconstruction system using feature correspondences and prior estimated finger model

The paper studies a 3D fingerprint reconstruction technique based on multi-view touchless fingerprint images. This technique offers a solution for 3D fingerprint image generation and application when only multi-view 2D images are available. However, the difficulties and stresses of 3D fingerprint reconstruction are the establishment of feature correspondences based on 2D touchless fingerprint images and the estimation of the finger shape model. In this paper, several popular used features, such as scale invariant feature transformation (SIFT) feature, ridge feature and minutiae, are employed for correspondences establishment. To extract these fingerprint features accurately, an improved fingerprint enhancement method has been proposed by polishing orientation and ridge frequency maps according to the characteristics of 2D touchless fingerprint images. Therefore, correspondences can be established by adopting hierarchical fingerprint matching approaches. Through an analysis of 440 3D point cloud finger data (220 fingers, 2 pictures each) collected by a 3D scanning technique, i.e., the structured light illumination (SLI) method, the finger shape model is estimated. It is found that the binary quadratic function is more suitable for the finger shape model than the other mixed model tested in this paper. In our experiments, the reconstruction accuracy is illustrated by constructing a cylinder. Furthermore, results obtained from different fingerprint feature correspondences are analyzed and compared to show which features are more suitable for 3D fingerprint images generation.     

A robust hybrid method for nonrigid image registration

A nonrigid registration method is proposed to automatically align two images by registering two sets of sparse features extracted from the images. Motivated by the paradigm of Robust Point Matching (RPM) algorithms [1] and [2], which were originally proposed for shape registration, we develop Robust Hybrid Image Matching (RHIM) algorithm by alternatively optimizing feature correspondence and spatial transformation for image registration. Our RHIM algorithm is built to be robust to feature extraction errors. A novel dynamic outlier rejection approach is described for removing outliers and a local refinement technique is applied to correct non-exactly matched correspondences arising from image noise and deformations. Experimental results demonstrate the robustness and accuracy of our method.