结合特征和时空关联的稳健图像匹配方法

提出了基于特征和时空关联的积相关图像匹配算法．首先，采用Bubble小波边缘检测方法提取实时图与基准图特征；其次，利用惯导位置信息平移基准图，实现序列实时图在其对应基准图中的相对位置相同；最后，基于匹配概率与匹配面积成正比原理叠加序列积相关矩阵，并确定匹配位置．该方法克服了因面积增大由几何失真导致的匹配概率下降的缺点，使匹配概率已精度得到提高，具有较好的匹配稳健性。     

基于直方图均衡化的景象匹配导航方法研究

针对基准图与实时图之间灰度分布差异对匹配结果的影响,将直方图调整与灰度相关算法相结合,提出了一种基于直方图均衡化预处理的景象匹配导航方法.首先对基准图与实时图的直方图进行调整,减少二者的灰度分布差异,然后采用积相关算法计算匹配结果.分析了景象匹配主要验证方法的优缺点,提出了一种实用的验证方法.利用该验证方法,在各种干扰...     

一种新的SAR图像分块匹配算法

当SAR图像噪声过强时,采用归一化积相关算法得到的相关面对比性较差,匹配结果可靠性降低。为解决此问题,给出一种新的SAR图像分块匹配算法。该算法将实时图分块并分别与基准图进行匹配,利用分块图像之间的相关性来找正确匹配点,增强了匹配结果的可靠性。实验证实该算法在实时图有强噪声的情况下依然可以正确匹配。     

基于相关面特征的多子区异谱图像匹配算法

为实现用于异谱图像之间的精确匹配,提出一种基于相关面特征的多子区关系约束匹配算法。首先在实时图上选择出边缘信息丰富的区域作为待匹配子区,然后进行相关匹配,分析相关面特征找到符合形态特征的多个个局部最大值位置,将各子区位置关系作为约束条件对得到的局部最大值位置进行聚类,对匹配结果进行综合和可信度判断。试验表明,该方法可有效提高多子区相关匹配方法的性能,具有更高的匹配概率和更好的图像尺度和旋转适应性。     

像素抽取加速的匹配算法

介绍了一种新颖的基于像素抽取加速的匹配算法,并给出了抽取像素数N选取的一般原则和方法,以及N和匹配时间、匹配概率、图像大小的关系.算法首先对待匹配图像进行网格化分,然后按照所划分好的网格抽取掉不必要的冗余信息进行匹配,从而大大降低了计算量.大量的仿真实验结果表明,在保证匹配概率相近的前提条件下,该算法与归一化积相关算法相比,匹配时间不足其1/10,而且在基准图大小固定时,随着实时图尺寸的增大,算法消耗匹配时间少的优势会越来越明显,甚至优于二层金字塔分解加速的匹配方法.     

基准图与实时图公因子提取方法研究与实验

基准图和实时图公共信息--公因子的提取是提高景象匹配准确率和定位精度的关键因素.通过对景象匹配误差原因的分析,提出一种有效提取基准图和实时图公因子的方法.该方法首先通过二阶高斯低通滤波算子将图像中同类景象区域化,其后通过Sobel梯度模算子,对图像中较稳定的区域边缘和区域内部轮廓线进行加权,同时滤除不稳定的局部直流分量影响,从而有效提取了基准图和实时图公因子.相关峰分析和多组真实图像实验证明了该方法的正确性和有效性.     

不同传感器图像的景象匹配区选取准则研究

景象匹配区的选取工作是影响光学景象匹配系统性能的重要因素,在分析影响景象匹配算法的配准概率和定位精度等因素的基础上,人们已经提出了一些实用的选取方法。文章首先对这些方法进行了研究,然后,从选取准则的角度对影响景象匹配区选取的因素进行了归纳分析,通过实验发现利用这些准则在进行实时图为红外图像、基准图为可见光图像的景象匹配区选取时存在一些问题,文章针对这些问题提出一种基于图像边缘密度与自匹配系数的景象匹配区选取准则。实验证明,该算法既保证了所选匹配区中的边缘信息量,又确保了所选区域具有较好的不相关性,采用粗选加精选的方式提高了选取速度。     

改进Hausdorff距离和粒子群算法在激光制导中的应用

为提高激光成像制导精度,实现遮挡条件下的有效识别,提出一种基于改进Hausdorff距离和粒子群算法的激光图像匹配算法。首先提取基准图和实时图的边缘特征;而后针对原始Hausdorff距离易受噪声、孤立点及遮挡影响的不足,提出一种自适应部分均值Hausdorff距离,并将其作为相似性测度;最后改进粒子群算法以完成搜索匹配,一方面提出混沌惯性权值以提高其搜索能力,另一方面通过引入混沌局部搜索避免算法过早收敛。实验结果表明,该算法不仅具有较高的匹配成功率,而且实时性较好。     

夜间图像匹配中的斑状噪声预处理

夜间图像匹配中,实时夜间图像中的斑状噪声影响了边缘相关匹配的性能。在分析了 斑状噪声特点的基础上,提出了一种新的基于标记的噪声预处理方法,先标记出图像中的斑状噪声点,在匹配时被标记的噪声点不参与相关统计,从而达到消除斑状噪声对匹配影响的目的。该方法与传统的中值滤波、均值滤波等方法相比,更能提高相关匹配的性能,试验结果验证了该方法的有效性。     

基于SVD-SURF的宽基线鲁棒景象匹配算法

提出一种基于SVD-SURF的宽基线鲁棒景象匹配算法。首先,在实时图与基准图奇异值分解的基础上构建SURF尺度空间,运用快速Hessian矩阵定位极值点;然后,计算出图像的64维SURF描述子;最后,通过Hessian矩阵迹进行特征点匹配,并利用RANSAC参数估计方法剔除出格点,从而实现位置参数的精确估计。实测航空图像序列位置估计实验表明了该景象匹配算法对图像的旋转、尺度变换及噪声不敏感,具有较强的实时性、精确性和鲁棒性。     

An efficient image-registration method based on probability density and global parallax

This paper is concerned with the problem of image registration of video sequence, to meet the accuracy and real-time requirements for video analysis and processing. Despite of the existence of sophisticated registration algorithms, it is still problematic to register images precisely with low computation complexity due to the lack of stable features and effective matching method. In this paper, a probability density gradient based interest point detector is employed to extract stable point features precisely. And a robust technique, namely, global parallax histogram based filter is proposed to discard outliers in the initial candidate matches set found by classical correlation method. The registration matrix can then be accurately and precisely estimated using a well adapted criterion. Several field tests are performed on complex and challenging images to assess the performance, including comparison to conventional algorithms, and both inter-frame registration results and statistical analysis of video sequence. These simulations validate the improvement of proposed method in accuracy and efficiency, and the robustness against camera motions, illumination variations, acquirement conditions, moving objects and image noise.     

A VLSI Architecture for Image Registration in Real Time

Image registration is an ubiquitous task occurring in countless image analysis applications. A dedicated implementation of image registration algorithms is the best approach to meet the intensive computation requirements of implementing image registration schemes in real time. This paper presents an efficient VLSI architecture for real-time implementation of image registration algorithms using an exhaustive search method. Normalized cross correlation function, mean square error, and blue screen technique algorithms are implemented for image registration. The architecture is based on a data flow design that allows sequential inputs but performs parallel processing. Based on the architecture, a programmable chip can be designed for image registration. Chips can be cascaded to achieve better performance and sizes of both the search and the reference image which can vary with time from a small to a very large value.     

红外搜索跟踪系统中实时图像配准的研究和实现

为实现在红外探测器运动的条件下地面弱小目标的快速搜索与跟踪,提出了一种基于相位相关法配准和角点法配准相结合的红外图像配准方法实现对红外图像的运动补偿。根据相位相关法配准和角点法配准的特点,首先利用相位相关法对运动中的红外图像进行粗配准,然后利用相位相关法配准的结果作为角点法配准的先验信息实现图像的高精度配准。实验结果表明该算法能够在不降低配准精度的条件下,实时地为红外搜索跟踪系统提供红外图像的位移信息,有效地提高了红外搜索跟踪系统对弱小目标的检测率和检测精度。     

Entropy-based dual-portal-to-3-DCT registration incorporating pixel correlation

For patient setup verification in external beam radiotherapy (EBRT) of prostate cancer, we developed an information theoretic registration framework, called the minimax entropy registration framework, to simultaneously and iteratively segment portal images and register them to three-dimensional (3-D) computed tomography (CT) image data. The registration framework has two steps, the max step and the min step, and evaluates appropriate entropies to estimate segmentations of the portal images and to find the transformation parameters. In the initial version of the algorithm (Bansal et al. 1999), we assumed image pixels to be independently distributed, an assumption not true in general. Thus, to better segment the portal images and to improve the accuracy of the estimated registration parameters, in this initial formulation of the problem, the correlation among pixel intensities is modeled using a one-dimensional Markov random process. Line processes are incorporated into the model to improve the estimation of segmentation of the portal images. In the max step, the principle of maximum entropy is invoked to estimate the probability distribution on the segmentations. The estimated distribution is then incorporated into the min step to estimate the registration parameters. Performance of the proposed framework is evaluated and compared to that of a mutual information-based registration algorithm using both simulated and real patient data. In the proposed registration framework, registration of the 3-D CT image and the portal images is guided by an estimated segmentation of the pelvic bone. However, as the prostate can move with respect to the pelvic structure, further localization of the prostate using ultrasound image data is required, an issue to be further explored in future.     

基于梯度相位相关的自动图像配准方法

图像配准是超分辨率重建、图像融合的基础。提出了非迭代的基于梯度相位相关的图像自动配准方法。首先分析了图像梯度和相位相关的理论,证明了提出方法的正确性;然后给出了图像配准的过程,并利用人工平移图像配准对该方法进行了实验验证。实验表明,该方法配准精度高,能有效地实现图像的自动配准。     

Entropy-based image registration method using the curvelet transform

Registration is a prerequisite for fusion of geometrically distorted images. Traditionally, intensity-based image registration methods are preferred to feature-based ones due to higher accuracy of the former than that of the latter. To reduce computational load, image registration is often carried out using the approximate-level coefficients of a wavelet-like transform. Directional selectivity of the transform and the objective function used for the coefficients play vital roles in the alignment process of images. This paper introduces an image registration algorithm that uses the approximate-level coefficients of the curvelet transform, directional selectivity of which is better than many wavelet-like transforms. A conditional entropy-based objective function is developed for registration using a suitable probabilistic model of the curvelet coefficients of images. Suitability of the probability distribution of the coefficients is validated using a standard method to assess goodness of fit. To align the distorted images, the affine transformation that possesses parameters related to the translation, rotation, scaling, and shearing is used. Extensive experimentations are carried out to test the performance of the proposed registration method considering that the images are synthetically or naturally distorted. Experimental results show that performance of the proposed registration method is superior to existing methods in terms of commonly used performance metrics.     

星空背景中目标识别算法研究

为了实时捕获混杂在天基背景中的运动目标,采用基于Fourier—Mellin图像配准与边缘提取相结合的差分算法。先采用Fourier—Mellin算法和相位相关算法求取图像的旋转角度及缩放和平移参数,得出配准点,再对连续采集的两帧图像配准、差分、二值化,将差分图像与后一帧边缘提取的图像进行逻辑乘,得到目标。仿真结果表明,该算法虚警率低,适合微型航天器多目标捕获的要求。     

基于轮廓特征的红外与可见光图像配准方法研究

红外光与可见光处于不同波段,其图像间的相关性较小.传统的基于特征的图像配准方法(如利用角点、边缘点等),在特征点选择时容易造成误匹配,这是由于有时特征点间的距离比较近造成的.针对此问题,本文提出了一种基于图像轮廓特征的红外与可见光图像配准方法.首先通过设置目标过滤器来提取明显的轮廓,再利用部分Hausdorff距离对轮廓进行匹配,计算出匹配轮廓对的面积和质心,并以此作为配准依据来对两种不同的图像进行配准.然后通过实验证明该方法的配准精度更高且克服了特征点误匹配的难点,这就可以解决刚性变换中红外与可见光图像间的配准问题.     

一种针对多通道GMTI的SAR复图像精确配准方法

提出了一种针对多通道GMTI的SAR复图像精确配准算法。首先,系统分析了配准误差对干涉相位的影响,推导出了对应配准误差的干涉相位Cramer-Rao界,并利用Monte Carlo仿真数据对配准误差的影响进行了量化。在此基础上,通过对待配准图像进行二维精确插值处理,生成模板图像库和相应的相关系数库,以适合多通道GMTI的复图像的相位相关为准则,找出模板图像库和相关系数库中与参考图像具有最大相位相关值的图像作为配准图像,从而实现多通道SAR复图像之间的精确配准。对基于所提算法的实测三通道SAR复图像进行慢动目标检测实验,结果表明所提算法能够提供多通道GMTI所需要的配准精度,证明了该算法的有效性和实用性。