电子稳像的分层位平面全局运动估计算法

提出一种分层位平面匹配的快速全局运动估计算法.算法充分利用多分辨率思想,在每一层选用不同位平面进行匹配以找到运动矢量|利用层内块运动的空间相关性和层间矢量传递性,来自适应地选择搜索起始点|同时设定阈值,对运动矢量满足准确度的子块,直接结束搜索|将所得到的各子块的运动矢量作为集合,根据其统计规律,找到出现概率最大的样本作为正确的全局运动矢量.实验表明,该算法计算速度快,且与块匹配全搜索相比,其误差小于半个像素,能够达到快速准确进行全局运动估计的能力.     

电子稳像的分层位平面全局运动估计算法

提出一种分层位平面匹配的快速全局运动估计算法.算法充分利用多分辨率思想,在每一层选用不同位平面进行匹配以找到运动矢量;利用层内块运动的空间相关性和层间矢量传递性,来自适应地选择搜索起始点;同时设定阈值,对运动矢量满足准确度的子块,直接结束搜索;将所得到的各子块的运动矢量作为集合,根据其统计规律,找到出现概率最大的样本作为正确的全局运动矢量.实验表明,该算法计算速度快,且与块匹配全搜索相比,其误差小于半个像素,能够达到快速准确进行全局运动估计的能力.     

改进块匹配宏块的快速传感器电子稳像方法

提出了一种改进块匹配宏块分布排列的快速传感器电子稳像算法,通过陀螺传感器测量摄像系统的抖动,利用小范围快速块匹配算法估计局部运动矢量,再运用最小二乘法解算全局运动矢量.小范围快速块匹配算法得到的局部运动矢量准确度高,仅需部分局部运动矢量即可准确解算出全局运动矢量.基于此在保证运动矢量准确度情况下,对块匹配宏块的分布排列进行了改进,从而减少匹配宏块数量加快算法速度.通过对宏块网,格模型的分析,得出对小范围快速块匹配算法进行宏块分布改进的方案,进而设计出快速传感器电子稳像算法.仿真及实验表明:运算时间提高89％左右,且算法准确度略高于改进前算法.     

用于像移探测的位平面匹配改进算法

像移探测作为电子稳像的重要的步骤,直接影响稳像的精度和速度。针对传统位平面匹配算法的局限,对各阶位平面的探测效果进行了综合测试,并提出了一种改进方案,即对位平面4,5经过下采样后组成一个合成位平面来进行匹配。研究了匹配块大小变化对位平面算法的影响,比较了相邻帧和固定帧两种参考帧选取方法的优劣。实验表明,改进的位平面匹配算法提高了位平面算法的稳健性,在没有增加计算量的前提下,算法精度达到了与全搜索块匹配法相当的程度。     

一种基于特征块匹配的电子稳像方法

提出一种基于特征模块匹配的图像稳定的算法.该算法自适应地搜索图像特征模块进行后续稳像操作.在稳像过程中,提出一种图像特征匹配的快速算法,匹配得到局部运动矢量,然后对匹配结果进行一致性检验,最后采用可变窗口宽度的均值滤波法对图像序列运动轨迹滤波.试验表明,该算法有效地稳定了图像序列,并且满足实时性要求.     

基于灰度投影的数字近景摄影立体影像匹配

根据数字近景摄影测量的特点,针对建筑物三维建模的需要,提出了基于灰度投影的影像匹配算法。该算法将影像行、列两个方向上的一维灰度投影,拓展为行、列、主对角线和次对角线四个方向上的一维灰度投影,计算左右影像的四个一维投影向量间的相似性测度的加权平均值作为最后的匹配测度。利用相对定向线性变换（RLT）算法来确定同名核线,以改进的动态定界法来控制待匹配点序列中后续点沿核线匹配的搜索范围,以减少匹配的搜索空间并提高匹配的准确度。利用该算法对建筑物数字近景摄影影像进行了实验,结果表明该算法具有较高的匹配速度和匹配准确率。     

基于Harris角点全局运动估计的场景锁定算法

场景锁定技术是视频跟踪领域的一个关键技术,需要对图像的全局运动进行估计,常用的运动估计算法由于计算量大、对噪声敏感等因素很难得到实际应用。为了减少运动估计的计算量,提高全局运动估计的精度,提出了一种基于Harris角点全局运动估计的场景锁定方法。将图像分成4×4的16个块,选取每个块中响应值最大的角点,以参考图像角点周围矩形块与待匹配图像进行匹配,然后利用RANSAC算法对角点进行一致性检测,利用最小二乘法解算全局运动参数,最后计算图像之间的累积运动。实验结果表明,该算法运动估计精度高,稳定性好,能较好地实现场景锁定。     

动态图像序列运动矢量估计算法的研究

运动估计在电子稳像系统中起重要的作用。介绍和比较了几种运动矢量估计算法。为了提高电子稳像系统的性能,提出了在设计电子稳像系统时选取运动矢量估计算法的原则。通过比较应用在航摄图像序列中的投影算法和匹配算法的性能,说明了如何应用运动矢量估计算法的原则。仿真结果表明,根据此原则选取的运动矢量估计算法应用于电子稳像系统是可行的。     

超高速高性能门控型三分幅相机

提出了一种改进块匹配宏块分布排列的快速传感器电子稳像算法,通过陀螺传感器测量摄像系统的抖动,利用小范围快速块匹配算法估计局部运动矢量,再运用最小二乘法解算全局运动矢量.小范围快速块匹配算法得到的局部运动矢量准确度高,仅需部分局部运动矢量即可准确解算出全局运动矢量.基于此在保证运动矢量准确度情况下,对块匹配宏块的分布排列进行了改进,从而减少匹配宏块数量加快算法速度.通过对宏块网格模型的分析,得出对小范围快速块匹配算法进行宏块分布改进的方案,进而设计出快速传感器电子稳像算法.仿真及实验表明:运算时间提高89%左右,且算法准确度略高于改进前算法.     

针对平移和旋转的快速电子稳像方法

针对视频图像中存在的平移抖动和旋转抖动,提出了一种基于灰度投影和模板匹配的电子稳像方法。用网格法搜索图像中灰度特征明显的区域,结合分象限法提高系统的鲁棒性,在不同坐标系下分别投影,纠正平移和旋转偏差。给出了模板制作方法和配准步骤。实验证明,该方法具有较高的精度和很高的处理速度,能够实时去除由视频图像抖动引起的噪声。     

多探测器拼接成像系统实时图像配准

依据已设计完成的基于同心球透镜的四镜头多探测器阵列拼接成像系统,对该系统图像拼接配准过程所采用的特征检测提取、特征向量匹配与筛选、空间变换模型参数估计等算法进行了研究。首先,采用Fast-Hessian检测子提取参考图像和待配准图像的特征点,并生成加速鲁棒特征(SURF)描述向量。接着,采用快速近似最近邻(FANN)逼近搜索算法获得初始的匹配点对,并对匹配点对特征向量的欧式距离进行排序。然后,参照成像系统光学设计参数设定合理的阈值,筛选并保留下较好的匹配点对。最后,提出了一种改进的渐进式抽样一致性(IPROSAC)算法对空间变换矩阵模型进行参数估计,从而得到参考图像与待配准图像的空间几何变换关系。实验结果表明:该算法对图像尺寸、旋转和光照变化都具有一定的不变性,特征匹配时间为0.542 s,配准变换时间0.031 s,配准误差精度小于0.1 pixel,可以满足成像系统关于图像配准实时性和准确性的要求,具有一定的工程应用价值。     

Analysis of motion tracking in echocardiographic image sequences: Influence of system geometry and point-spread function

This paper focuses on motion tracking in echocardiographic ultrasound images. The difficulty of this task is related to the fact that echographic image formation induces decorrelation between the underlying motion of tissue and the observed speckle motion. Since Meunier’s seminal work, this phenomenon has been investigated in many simulation studies as part of speckle tracking or optical flow-based motion estimation techniques. Most of these studies modeled image formation using a linear convolution approach, where the system point-spread function (PSF) was spatially invariant and the probe geometry was linear. While these assumptions are valid over a small spatial area, they constitute an oversimplification when a complete image is considered. Indeed, echocardiographic acquisition geometry relies on sectorial probes and the system PSF is not perfectly invariant, even if dynamic focusing is performed.This study investigated the influence of sectorial geometry and spatially varying PSF on speckle tracking. This was done by simulating a typical 64 elements, cardiac probe operating at 3.5 MHz frequency, using the simulation software Field II. This simulation first allowed quantification of the decorrelation induced by the system between two images when simple motion such as translation or incompressible deformation was applied. We then quantified the influence of decorrelation on speckle tracking accuracy using a conventional block matching (BM) algorithm and a bilinear deformable block matching (BDBM) algorithm. In echocardiography, motion estimation is usually performed on reconstructed images where the initial sectorial (i.e., polar) data are interpolated on a cartesian grid. We therefore studied the influence of sectorial acquisition geometry, by performing block matching on cartesian and polar data.Simulation results show that decorrelation is spatially variant and depends on the position of the region where motion takes place relative to the probe. Previous studies did not consider translation in their experiments, since their simulation model (spatially invariant PSF and linear probe) yields by definition no decorrelation. On the opposite, our realistic simulation settings (i.e., sectorial probe and realistic beamforming) show that translation yields decorrelation, particularly when translation is large (above 6 mm) and when the moving regions is located close to the probe (distance to probe less than 50 mm).The tracking accuracy study shows that tracking errors are larger for the usual cartesian data, whatever the estimation algorithm, indicating that speckle tracking is more reliable when based on the unconverted polar data: for axial translations in the range 0-10 mm, the maximum error associated to conventional block matching (BM) is 4.2 mm when using cartesian data and 1.8 mm for polar data. The corresponding errors are 1.8 mm (cartesian data) and 0.4 mm (polar data) for an applied deformation in the range 0-10%. We also show that accuracy is improved by using the bilinear deformable block matching (BDBM) algorithm. For translation, the maximum error associated to the bilinear deformable block matching is indeed 3.6 mm (cartesian data) and 1.2 mm (polar data). Regarding deformation, the error is 0.7 mm (cartesian data) and 0.3 mm (polar data). These figures also indicates that the larger improvement brought by the bilinear deformable block matching over standard block matching logically takes place when deformation on cartesian data is considered (the error drops from 1.8 to 0.7 mm is this case).We give a preliminary evaluation of this framework on a cardiac sequence acquired with a Toshiba Powervision 6000 imaging system using a probe operating at 3.25 MHz. As ground truth reference motion is not available in this case, motion estimation performance was evaluated by comparing a reference image (i.e., the first image of the sequence) and the subsequent images after motion compensation has been applied. The comparison was quantified by computing the normalized correlation between the reference and the motion-compensated images. The obtained results are consistent with the simulation data: correlation is smaller for cartesian data, whatever the estimation algorithm. The correlation associated to the conventional block matching (BM) is in the range 0.45-0.02 when using cartesian data and in the range 0.65-0.2 for polar data. The corresponding correlation ranges for the bilinear deformable block matching are 0.98-0.2 and 0.98-0.55. In the same way these figures indicate that the bilinear deformable block matching yield a larger improvement when cartesian data are considered (correlation range increases from 0.45-0.02 to 0.98-0.2 in this case).     

一种补偿平移与旋转运动的快速电子稳像算法

针对视频图像序列的非稳特性,研究了电子稳像算法中灰度投影算法和块匹配算法各自的不足之处,提出了一种快速补偿视频图像序列间平移及旋转运动的稳定成像算法.该算法先采用灰度投影算法估计并补偿视频图像序列间的平移运动,再利用拉普拉斯变换在靠近图像的边缘区域选取几个具有明显特征的小块,运用块匹配算法进行匹配,计算并补偿其旋转运动量,以得到稳定的视频图像序列.通过理论分析和实验验证,表明这种稳像算法具有速度快、准确度高的特点.     

A coarse-to-fine kernel matching approach for mean-shift based visual tracking

Mean shift is an efficient pattern match algorithm. It is widely used in visual tracking fields since it need not perform whole search in the image space. It employs gradient optimization method to reduce the time of feature matching and realize rapid object localization, and uses Bhattacharyya coefficient as the similarity measure between object template and candidate template. This thesis presents a mean shift algorithm based on coarse-to-fine search for the best kernel matching. This paper researches for object tracking with large motion area based on mean shift. To realize efficient tracking of such an object, we present a kernel matching method from coarseness to fine. If the motion areas of the object between two frames are very large and they are not overlapped in image space, then the traditional mean shift method can only obtain local optimal value by iterative computing in the old object window area, so the real tracking position cannot be obtained and the object tracking will be disabled. Our proposed algorithm can efficiently use a similarity measure function to realize the rough location of motion object, then use mean shift method to obtain the accurate local optimal value by iterative computing, which successfully realizes object tracking with large motion. Experimental results show its good performance in accuracy and speed when compared with background-weighted histogram algorithm in the literature.     

基于聚类的相位相关运动估计方法

针对相机运动引起的图像序列运动的问题,提出了一种基于聚类的相位相关块匹配运动估计算法。利用Harris算子分别在相邻帧图像上检测角点,以参考图像角点为中心选取一个矩形块,将块匹配法与相位相关相结合来计算图像间的运动矢量。最后,对获得的多个块的平移量,进行空间聚类从而选取运动估计比较准确的点。实验结果表明：该算法配准精度能达到亚像素,稳定性较好。     

基于运动检测的多车辆跟踪方法研究

针对交通监控场景中多目标粘连造成跟踪上的困难和前后两帧车辆关联困难,提出了区域运动相似性分割方法和相似度关联矩阵的解决方案;在运动目标检测过程中, 首先使用背景差分法提取运动区域,经过消除缺口、空洞和分离等处理,在运动区域所在范围内进行块匹配搜索和局部光流计算区域运动矢量,然后使用模糊聚类方法对运动矢量区域融合,完整的分割出粘连运动目标;在目标跟踪部分,目标跟踪建立在目标关联的基础上,提出建立连续两帧目标间距离和局部二元模式相似度关联矩阵的方法进行运动目标标定,从而实现多目标关联;使用公共视频库的图像序列进行测试,所提算法都能实现连续的跟踪和准确的运动目标分割,且处理速度快,表明了算法具有鲁棒性和适用性。     

基于一维标定物和改进进化策略的相机标定

为了解决运动参数光电探测过程中的相机标定问题,制作一种两端及中间各安装一个红外反光标记球的一维标定物.不需要其它复杂标定装置,只要将这种特制的一维标定物在测量空间内多次随意移动并摄取其图像,即可实现标定.算法首先假定主点位于像面中心附近的某个位置,再求出焦距、旋转矩阵、平移向量和比例因子,最后通过评价函数将相机标定转换成寻找两相机最佳主点对的非线性最小化问题.在传统进化策略中引进个体的自我改进系数、个体间距离等概念,提出了求取子代个体间的欧式距离并排序的方法,设计了搜索最佳主点对的改进型进化策略算法.与传统标定方法相比,基于一维标定物的方法克服了多相机场合的遮挡问题,改进进化策略的引入打破了一维标定物需做某种特殊运动的限制,使一维标定物自由运动时相机内、外参数的同时求解成为可能,改进的模拟退火进化策略改善了算法的全局收敛性能并加快了收敛速度.     

一种改进的块匹配运动目标识别算法

鉴于传统的块匹配法在图像特征提取、匹配以及搜索等方面有诸多不足,复杂的边缘提取处理与基于灰度的匹配准则都使得系统的处理速度与精度降低,为了使识别处理更加快速精确,提出了一种改进的块匹配识别算法。通过拉普拉斯算子对图像特征进行增强,提高图像的特征信息;实验表明,针对320像素240像素大小的图像,改进算法的特征增强计算时间在Matlab中平均约为0005 4 s。     

一种基于自适应补偿的快速帧速率上转换算法

提出一种基于自适应补偿的快速帧速率上转换算法.算法在塔型结构数据上进行运动估计并利用相邻块运动矢量对上层传递矢量进行修正,减少计算量的同时获得了平滑的运动矢量场.在匹配搜索过程中采用动态调整搜索窗策略,避免了过搜索和搜索不足的问题.运动补偿克服了传统的补偿算法仅采用一种插值方法的不足,根据运动矢量的可靠性分别采用了3种不同的插值方法.为了减少块边缘的失真,采用了重叠块运动补偿的插值方法.在遮挡区域,设计了加权多候选运动矢量插值方法,对前后两帧补偿结果分别赋予不同的权值以减少失真.实验结果表明,该算法与传统算法相比不仅可以大幅度降低计算量,且插值图像的质量有所提高.     

Two-step image registration by artificial immune system and chamfer matching

Image registration is the precondition and foundation in the fusion of multi-source image data. A two-step approach based on artificial immune system and chamfer matching to register images from different types of sensors is presented. In the first step, it extracts the large edges and takes chamfer distance between the input image and the reference image as similarity measure and uses artificial immune network algorithm to speed up the searching of the initial transformation parameters. In the second step, an area-based method is utilized to refine the initial transformation and enhance the registration accuracy. Experimental results show that the proposed approach is a promising method for registration of multi-sensor images.