基于迭代的非刚性点阵配准算法

提出的非刚性点阵配准算法把一种鲁棒性全局和局部多特征用于对应关系评估,并结合高斯混合模型进行空间变换更新。首先,定义两个距离特征,分别测定两个点阵间的全局和局部几何结构差异,这两个特征形成了一种基于能量优化方程的多特征,通过最小化此多特征,可以灵活地评估点阵间的对应关系。其次,设计一种基于高斯混合模型的空间变换能量方程,同时借助L-2距离最小化方法将其最小化,以此改善空间变换更新。最后,采用轮廓配准和图像特征点配准测试了算法的性能,并与其他4种先进方法进行了对比,该算法在大部分实验中展现了最好的配准效果。     

基于双特征高斯混合模型和双约束空间变换的配准

非刚性点集配准是当前多个领域中的一项重要研究问题.现今流行的配准算法通常使用基于单一特征的对应关系评估与包含单一约束条件的空间变换更新,而单特征与单约束限制了其配准效果与应用领域.提出了一种基于双特征高斯混合模型和双约束空间变换的非刚性点集配准算法.首先定义了双特征描述子,并用全局特征和局部特征构建它;随后,基于此描述子将高斯混合模型改进为双特征高斯混合模型.定义了局部结构约束项,并与全局结构约束项分别维护点集在进行空间变换更新时的局部与全局结构稳定.通过交替进行基于双特征高斯混合模型评估点集之间的对应关系和基于高斯径向基函数（Gaussian radial basis function）更新双约束空间变换,使该算法准确地完成非刚性点集配准.通过人造点集配准、CMU序列图像配准、遥感图像配准、IMM人脸数据配准和真实图像特征点配准对该算法进行了性能测试,同时也与当前流行的8种算法进行了性能比较实验,该算法展现出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.     

基于模糊形状上下文与局部向量相似性约束的配准算法

非刚性点集配准研究是模式识别领域的一项重要基础研究.本文在当前流行的非刚性点集配准算法的基础上提出了两个主要贡献:1)模糊形状上下文(Fuzzy shape context, FSC)特征;2)基于局部向量特征的局部空间向量相似性约束项.本文首先进行基于特征互补的对应关系评估,在这一步骤中定义了模糊形状上下文特征,然后基于模糊形状上下文特征差异和全局特征差异设计了特征互补的高斯混合模型.其次,进行基于约束互补的空间变化更新.在这一步骤中,定义了局部向量特征,建立了局部空间向量相似性约束项.本文算法通过使用特征互补的高斯混合模型进行对应关系评估,并将配准问题转化为可以用期望最大化(Expectation maximization, EM)算法解决的参数优化问题,通过创建包含局部空间向量相似性约束项的能量方程优化了空间变换更新.本文首先测试了模糊形状上下文特征的检索率,然后采用公开数据集测试了算法在点集配准与图像配准的性能.在与当前流行的十种算法的对比实验中,本文算法均给出了精确的配准结果,并在大部分实验中精度超过了当前流行算法.     

利用混合特征的多视角遥感图像配准

目的 多视角遥感图像配准是遥感图像处理领域的一项关键技术,其目的是精确获取图像间被测区域发生的几何变换关系。由于航拍视角变化以及地物的空间分布和几何形状的复杂度,多视角遥感图像间会产生非刚性畸变问题,增加了图像配准的难度,为此本文提出一种利用遥感图像SIFT（scale-invariant feature transform）特征点阵的全局和局部几何结构特征进行多视角遥感图像配准的算法。方法 通过增加对SIFT点阵的几何结构特征描述以及利用SIFT点阵间全局与局部几何结构特征的互补关系,提升存在非刚性畸变的多视角遥感图像配准精度。 结果 实验使用谷歌地球的卫星影像数据以及无人机航拍遥感数据对本文算法进行了测试,并与3种同类算法（SIFT、SURF（speeded-up robust features）、CPD（coherent point drift））进行对比实验,本文算法在存在非刚性畸变的多视角遥感图像配准中能够有效地提升SIFT特征点阵的配准精度,从而获得更加准确的多视角遥感图像配准结果。结论 本文实现了一种结合SIFT特征点阵的全局和局部几何结构特征进行多视角遥感图像配准的算法,实验结果表明了该方法对存在非刚性畸变的多视角遥感图像能够有效地进行配准,可适用于同源多视角情况下的遥感图像配准问题。     

基于多薄板样条的多视角非刚性配准算法EI北大核心CSCD

为解决多视角配准中带有低频非刚性形变的深度数据容易产生累积误差、重叠区域未对齐等问题,提出一种基于多薄板样条的多视角非刚性配准算法.首先通过局部迭代最近点刚性配准算法得到重叠视角深度数据之间的对应点;然后基于多薄板样条的全局优化能量公式为每个视角求解一个薄板样条变换,使所有对应点之间距离的平方和最小;最后将优化后的薄板样条变换应用于每个视角的深度数据.通过在优化模型中引入初始点位置约束,该算法能使配准后的数据尽可能保持初始形状.为了加快求解速度,迭代地求解每个薄板样条变换,并且在优化过程中增量式地引入径向基函数.实验室结果表明,文中算法有较高的精度和效率,能够有效地减少累积误差并且提升重叠区域的对齐效果.     

基于多薄板样条的多视角非刚性配准算法

为解决多视角配准中带有低频非刚性形变的深度数据容易产生累积误差、重叠区域未对齐等问题,提出一种基于多薄板样条的多视角非刚性配准算法.首先通过局部迭代最近点刚性配准算法得到重叠视角深度数据之间的对应点;然后基于多薄板样条的全局优化能量公式为每个视角求解一个薄板样条变换,使所有对应点之间距离的平方和最小;最后将优化后的薄板样条变换应用于每个视角的深度数据.通过在优化模型中引入初始点位置约束,该算法能使配准后的数据尽可能保持初始形状.为了加快求解速度,迭代地求解每个薄板样条变换,并且在优化过程中增量式地引入径向基函数.实验室结果表明,文中算法有较高的精度和效率,能够有效地减少累积误差并且提升重叠区域的对齐效果.     

非介入式手术导航中医学图像配准算法

提出一种用于非介入手术导航中基于自由变形模型的多模态医学图像非刚性配准方法,对术前MRI/CT和术中超声图像中都可见的血管结构进行配准.当图像对准时,一种图像中的血管中心点对应着另一种图像下灰度脊点;对于全局变换采用刚性变换、局部形变采用基于函数控制B样条的自由变形模型来描述;采用遗传算法和共轭梯度法相结合达到最小化目标函数.将文中算法应用于体模和临床数据,在配准精度和收敛速度上都取得了良好的效果.     

基于轮廓的多设备医学图象的刚性配准

提出了一种基于轮廓的多设备医学图象的刚性配准算法。在半自动轮廓搜索的基础上，运用迭代最近邻点方法搜索轮廓间的对应点，最小化代价函数并获得最佳刚性变换，实现图象的精确配准。通过多次选择初始变换的搜索策略，解决了搜索过程中出现的局部极小值问题。实验表明该方法能有效地实现多设备医学图象的配准。     

基于改进Demons算法的三维肺部医学影像配准研究*

为实现相同个体在不同呼吸状态下产生较大形变的三维肺部医学影像配准,提出一种基于改进Demons算法的精确有效配准方案。首先,对待配准影像进行全局非刚性配准。通过尺度不变特征变算法对影像进行特征点提取与匹配,根据匹配结果计算变换参数,完成全局配准;其次,利用改进Demons算法对全局配准后的影像进行局部非刚性配准。使用改进的方案实现了人体肺部影像配准,并且肺部整体轮廓以及内部组织的配准结果较理想。配准前,影像间的均方误差值为25835.3,经配准后影像间均方误差值降为3726.31,均方误差值下降率为85.58%。提出的方案能够有效配准三维肺部影像,为对肺部呼吸运动估计以及呼吸功能分析提供良好的基础。     

基于变分贝叶斯层次概率模型的非刚性点集配准

非刚性点集配准是计算机视觉和模式识别领域的基础研究问题,现今的非刚性点集配准算法在存在大量离群点、噪声、点集对应关系缺失、旋转和形变情况下,不能非常准确地评估出两个点集间的对应关系.本文通过交替执行点集对应关系评估和空间转换更新两个步骤来逐步恢复点集间一一对应关系.在对应关系评估步骤,首先本文基于有限重尾学生t分布隐变量混合模型(student-t distribution Latent Mixture Model,简称TLMM)构造变分贝叶斯层次概率模型(Variational Bayes Hierarchical Probability Model,简称VBHPM)并将其分为对应关系评估组件和离群点聚合组件,分别用来评估点集间对应关系和聚合离群点,同时使用贝叶斯线性回归方法来抵抗噪声的干扰.其次本文加入Dirichlet先验分布来动态调节模型的混合比例,为对应关系缺失的点分配较小的混合比例以保持点集结构的稳定性.在空间转换更新步骤,本文基于变分贝叶斯(Variational Bayes,简称VB)框架来迭代更新模型参数,并提出树状平均场因式分解方法来维持模型参数间的依赖关系,以获得更紧致的变分下界.此外,本文提出自适应全局-局部约束策略来维持点集间结构的稳定性,抵抗形变和旋转影响的同时实现从局部到全局的约束过程.最后,本文采用了双阶段先验退火方案,在退火过程中使用Gamma先验分布来动态调节精度,实现由粗到精的配准过程.在实验部分,本文不仅测试了VBHPM的性能,而且展示了点集和图像配准的结果,并与当前流行的13种算法进行了比较,VBHPM皆能展现较准确的配准结果和较高的精度.     

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.     

Robust Non-rigid Registration Based on Affine ICP Algorithm and Part-Based Method

The classical affine iterative closest point (ICP) algorithm is fast and accurate for affine registration between two point sets, but it is easy to fall into a local minimum. As an extension of the classical affine registration algorithm, this paper first proposes an affine ICP algorithm based on control point guided, and then applies this new method to establish a robust non-rigid registration algorithm based on local affine registration. The algorithm uses a hierarchical iterative method to complete the point set non-rigid registration from coarse to fine. In each iteration, the sub data point sets and sub model point sets are divided, meanwhile, the shape control points of each sub point set are updated. Then we use the control point guided affine ICP algorithm to solve the local affine transformation between the corresponding sub point sets. Next, the local affine transformation obtained by the previous step is used to update the sub data point sets and their shape control point sets. Experimental results demonstrate that the accuracy and convergence of our algorithm are greatly improved compared with the traditional point set non-rigid registration algorithms.     

深度特征融合的非刚性三维形状局部匹配

针对海量、异构三维形状匹配与智能检索技术的需求,提出了一种基于级联卷积神经网络(F-PointCNN)深度特征融合的三维形状局部匹配方法.首先,采用特征袋模型,提出几何图像表示方法,该几何图像不仅能够有效区分同类异构的非刚性三维模型,而且能够揭示大尺度不完整三维模型的结构相似性.其次,构建级联卷积神经网络学习框架F-PointCNN,其中,BoF-CNN从几何图像中学习深度全局特征,建立融合局部特征与全局特征的点特征表示;进而对Point-CNN进行点特征的细化与提纯,生成具有丰富信息的深度融合特征,有效提高形状特征的区分性与鲁棒性.最终,通过交叉矩阵度量方法高效实现非刚性三维模型的局部形状匹配.在公开的非刚性三维模型数据库的实验结果表明,该方法提取的特征在大尺度变换的形状分类及局部形状匹配中具有更强的识别力与更高的匹配精度.     

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     

A fast and fully automatic registration approach based on point features for multi-source remote-sensing images

Automatic registration of multi-source remote-sensing images is a difficult task as it must deal with the varying illuminations and resolutions of the images, different perspectives and the local deformations within the images. This paper proposes a fully automatic and fast non-rigid image registration technique that addresses those issues. The proposed technique performs a pre-registration process that coarsely aligns the input image to the reference image by automatically detecting their matching points by using the scale invariant feature transform (SIFT) method and an affine transformation model. Once the coarse registration is completed, it performs a fine-scale registration process based on a piecewise linear transformation technique using feature points that are detected by the Harris corner detector. The registration process firstly finds in succession, tie point pairs between the input and the reference image by detecting Harris corners and applying a cross-matching strategy based on a wavelet pyramid for a fast search speed. Tie point pairs with large errors are pruned by an error-checking step. The input image is then rectified by using triangulated irregular networks (TINs) to deal with irregular local deformations caused by the fluctuation of the terrain. For each triangular facet of the TIN, affine transformations are estimated and applied for rectification. Experiments with Quickbird, SPOT5, SPOT4, TM remote-sensing images of the Hangzhou area in China demonstrate the efficiency and the accuracy of the proposed technique for multi-source remote-sensing image registration.