Image registration by local histogram matching

We previously presented an image registration method, referred to hierarchical attribute matching mechanism for elastic registration (HAMMER), which demonstrated relatively high accuracy in inter-subject registration of MR brain images. However, the HAMMER algorithm requires the pre-segmentation of brain tissues, since the attribute vectors used to hierarchically match the corresponding pairs of points are defined from the segmented image. In many applications, the segmentation of tissues might be difficult, unreliable or even impossible to complete, which potentially limits the use of the HAMMER algorithm in more generalized applications. To overcome this limitation, we have used local spatial intensity histograms to design a new type of attribute vector for each point in an intensity image. The histogram-based attribute vector is rotationally invariant, and importantly it also captures spatial information by integrating a number of local intensity histograms from multi-resolution images of original intensity image. The new attribute vectors are able to determine the corresponding points across individual images. Therefore, by hierarchically matching new attribute vectors, the proposed method can perform as successfully as the previous HAMMER algorithm did in registering MR brain images, while providing more generalized applications in registering images of various organs. Experimental results show good performance of the proposed method in registering MR brain images, DTI brain images, CT pelvis images, and MR mouse images.     

视频图象序列的实时镶嵌技术研究

阐述了一种快速而高效的由视频图象或视频图象序列生成全景的配准方法,为了估计图象配准的校正参数,该方法计算伪运动矢量,这些伪运动矢量是光流在每一选定象素处的粗略估计,使用方法,实现了一个在低价PC上就能实时创建和显示全景图像的软件。     

Image registration by compression

Image registration consists in finding the transformation that brings one image into the best possible spatial correspondence with another image. In this paper, we present a new framework for image registration based on compression. The basic idea underlying our approach is the conjecture that two images are correctly registered when we can maximally compress one image given the information in the other. The contribution of this paper is twofold. First, we show that image registration can be formulated as a compression problem. Second, we demonstrate the good performance of the similarity metric, introduced by Li et al., in image registration. Two different approaches for the computation of this similarity metric are described: the Kolmogorov version, computed using standard real-world compressors, and the Shannon version, calculated from an estimation of the entropy rate of the images.     

基于分块-空间聚类的图像配准算法

模板匹配算法应用广泛,但不能判断配准结果是否正确,也无法比较不同像对配准结果的准确程度。提出无变形、无旋转情况下分块-空间聚类的图像配准算法,将基准图分块在参考图上配准从而获得基准图的多个配准位置,并对这些位置进行空间聚类从而计算基准图的最后配准位置,并评估配准质量。试验表明该算法配准准确度高,能够正确评估配准质量并比较不同像对配准结果的准确程度。     

运动估值的实用快速算法

现有的许多有关运动估值的快速算法,都存在着匹配速度快与匹配精度差的矛盾。文章在分析已有典型快速算法优缺点的基础上,提出了解决这一矛盾的分步逼近的新算法——“迂回逼近法”;算法选择了快捷和更为准确的搜索路径,且对程序的实现技术作了有效改进,其最终匹配结果具有全匹配算法的精度和典型快速算法的速度。文中说明了算法原理,程序技术和对比实验结果。     

基于全搜索块匹配法的电子图像稳定

针对摄像机无意抖动引起的电子图象不稳定,文中使用一种基于全搜索块匹配的运动估计算法对图象序列进行补偿：通过对运动估计搜索算法的改进,既保持了传统全搜索法的精度,又大大缩短了运算时间：试验表明,文中算法对于去除电子图像序列抖动有良好的效果。     

航摄动态图像序列稳定技术仿真研究

通过分析块匹配算法的优缺点,采用一种其改进的算法应用于动态图像序列的运动矢量检测。该算法先分别把参考图像和当前图像分成若干个运动矢量探测区域,然后在各子区域选取一定数量的代表点进行相关匹配,求出各子区域的局部运动矢量,对所得到的运动矢量进行分析后,决定全局运动矢量。采用这种方法既能保持低的计算量,同时又具有较高的运动矢量检测性能。分析了影响图像的补偿精度主要因素,提出了相应的对策。用该算法对红外航摄图像序列进行稳定仿真试验,证明了该算法用于动态图像序列稳定的有效性。     

A new image registration scheme based on curvature scale space curve matching

We propose a new image registration scheme for remote sensing images. This scheme includes three steps in sequence. First, a segmentation process is performed on the input image pair. Then the boundaries of the segmented regions in two images are extracted and matched. These matched regions are called confidence regions. Finally, a non-linear optimization is performed in the matched regions only to obtain a global set of transform parameters. Experiments show that this scheme is more robust and converges faster than registration of the original image pair. We also develop a new curve-matching algorithm based on curvature scale space to facilitate the second step.     

Marker-less registration based on template tracking for augmented reality

Accurate 3D registration is a key issue in the Augmented Reality (AR) applications, particularly where are no markers placed manually. In this paper, an efficient markerless registration algorithm is presented for both outdoor and indoor AR system. This algorithm first calculates the correspondences among frames using fixed region tracking, and then estimates the motion parameters on projective transformation following the homography of the tracked region. To achieve the illumination insensitive tracking, the illumination parameters are solved jointly with motion parameters in each step. Based on the perspective motion parameters of the tracked region, the 3D registration, the camera’s pose and position, can be calculated with calibrated intrinsic parameters. A marker-less AR system is described using this algorithm, and the system architecture and working flow are also proposed. Experimental results with comparison quantitatively demonstrate the correctness of the theoretical analysis and the robustness of the registration algorithm.

不确定时延环境的图像序列预测理论

该文提出的“不确定时延环境的图像序列预测理论”从图像通信系统目端的角度出发,可以将接收端的不连续序列图像构造成连续的视频数据。即使图像源端发送出帧速率小于24帧每秒的图像序列（如每秒15帧）,在复现端仍然能够看到连续的视频图像。     

Nonlocal Image and Movie Denoising

Neighborhood filters are nonlocal image and movie filters which reduce the noise by averaging similar pixels. The first object of the paper is to present a unified theory of these filters and reliable criteria to compare them to other filter classes. A CCD noise model will be presented justifying the involvement of neighborhood filters. A classification of neighborhood filters will be proposed, including classical image and movie denoising methods and discussing further a recently introduced neighborhood filter, NL-means. In order to compare denoising methods three principles will be discussed. The first principle, “method noise”, specifies that only noise must be removed from an image. A second principle will be introduced, “noise to noise”, according to which a denoising method must transform a white noise into a white noise. Contrarily to “method noise”, this principle, which characterizes artifact-free methods, eliminates any subjectivity and can be checked by mathematical arguments and Fourier analysis. “Noise to noise” will be proven to rule out most denoising methods, with the exception of neighborhood filters. This is why a third and new comparison principle, the “statistical optimality”, is needed and will be introduced to compare the performance of all neighborhood filters. The three principles will be applied to compare ten different image and movie denoising methods. It will be first shown that only wavelet thresholding methods and NL-means give an acceptable method noise. Second, that neighborhood filters are the only ones to satisfy the “noise to noise” principle. Third, that among them NL-means is closest to statistical optimality. A particular attention will be paid to the application of the statistical optimality criterion for movie denoising methods. It will be pointed out that current movie denoising methods are motion compensated neighborhood filters. This amounts to say that they are neighborhood filters and that the ideal neighborhood of a pixel is its trajectory. Unfortunately the aperture problem makes it impossible to estimate ground true trajectories. It will be demonstrated that computing trajectories and restricting the neighborhood to them is harmful for denoising purposes and that space-time NL-means preserves more movie details.     

基于双视线特征感知编组的影像匹配方法

与点特征相比,高级别的特征,特别是自由形状线特征包含了更加丰富的信息,这对于其后的影像解译和表面重建等处理是更为有利的。针对传统自由形状线特征匹配方法的弊端——匹配成功的线特征数量较少,引入了感知编组理论,提出了一种基于双视线特征感知编组的影像匹配方法。实验证明该方法不仅可以提高成功匹配的自由形状线特征数量,还可增加自由形状线特征的长度,方便后续人工地物（如建筑物）的提取。     

Image registration by local approximation methods

Image registration is approached as an approximation problem. Two locally sensitive transformation functions are proposed for image registration. These transformation functions are obtained by the weighted least-squares method and the local weighted mean method. The former is a global method and uses information about all control points to establish correspondence between local areas in the images; nearby control points are, however, given higher weights to make the process locally sensitive. The latter is a local method and uses information about local control points only to register local areas in the images.     

Image registration using Markov random coefficient and geometric transformation fields

Image registration is central to different applications such as medical analysis, biomedical systems, and image guidance. In this paper we propose a new algorithm for multimodal image registration. A Bayesian formulation is presented in which a likelihood term is defined using an observation model based on coefficient and geometric fields. These coefficients, which represent the local intensity polynomial transformations, as the local geometric transformations, are modeled as prior information by means of Markov random fields. This probabilistic approach allows one to find optimal estimators by minimizing an energy function in terms of both fields, making the registration between the images possible.     

图像序列运动估计技术综述

图像序列运动估计是通过对图像序列的分析,得到图像全局运动量或局部物体运动量的一种重要技术方法,它已经广泛应用于国民经济的很多领域中.在对图像序列运动估计定义的基础上,首先将图像序列运动估计算法分为图像块法、像素法、特征法和相位法等四类,对各类方法中主要算法的基本原理进行了详尽的阐述,并比较了各算法的优缺点;然后探讨了图像序列运动估计算法在视频编码、电子稳像和图像配准等领域内的应用;最后展望了图像序列运动估计技术的前景和发展趋势.     

Deformable registration using patch-wise shape matching

We present a novel approach for non-rigid registration of partially overlapping surfaces acquired from a deforming object. To allow for large and general deformations our method employs a nonlinear physics-inspired deformation model, which has been designed with a particular focus on robustness and performance. We discretize the surface into a set of overlapping patches, for each of which an optimal rigid motion is found and interpolated faithfully using dual quaternion blending. Using this discretization we can formulate the two components of our objective function—a fitting and a regularization term—as a combined global shape matching problem, which can be solved through a very robust numerical approach. Interleaving the optimization with successive patch refinement results in an efficient hierarchical coarse-to-fine optimization. Compared to other approaches our as-rigid-as-possible deformation model is faster, causes less distortion, and gives more accurate fitting results.     

基于PC的实时超声全景成像系统中的图像配准

图像配准技术是超声宽视野成像技术的核心。结合现有的超声宽视野图像配准算法和PC平台的特点,通过匹配模板筛选、局部运动矢量评价、加权最小二乘等步骤,进一步改进和优化了配准的过程,提高了超声图像配准的鲁棒性和实时性。