Image Correlation with Geometric Distortion Part 1: Acquisition Performance

The degradation in acquisition probability that occurs when cross correlation is used to determine the offset of two images of the same scene that differ by a relative geometric distortion is presented. The geometric distortions considered can be represented by a general affine transformation of image coordinates. The analysis shows that for a given geometric distortion there is an image size and shape that minimizes the probability of false acquisition. The results are derived for images modeled as random patterns with arbitrary auto-correlation functions. The results are illustrated for images with Gaussian autocorrelation functions.     

Optimum Windows for Image Registration

Two-dimensional cross-correlation techniques are applied to the problem of image registration under the assumption of small geometric distortion. Optimum window functions are derived for two performance measures of interest: peak-to-sidelobe ratio and mean-square registration error. The latter is examined in terms of the contribution caused by noise and the contribution caused by geometric distortion. A generalized Lagrange multiplier approach is used to derive approximate solutions assuming random images. The case of Gaussian autocorrelation functions is examined in detail. Results of applying the theoretically derived window functions to real data are presented, showing significant improvement in correlator performance.     

Optimum Filters for Image Registration

Two-dimensional cross-correlation techniques are applied to the problem of image registration under the assumption of small geometric distortion. Optimum filter functions for use with arbitrary window functions are derived for two performance measures of interest: peak-to-sidelobe ratio, and mean- square registration error; the latter is examined in terms of the contribution due to distortion and the contribution due to noise. A generalized Lagrange multiplier approach is used to derive at least approximate ate solutions to the case where the images are modeled as random processes and to the case where the images are assumed to be deterministic.     

一种局部自适应内窥镜图像降噪模型

在内窥镜图像处理中,如何消除图像中的噪声一直是个热点问题。由于图像二进小波变换在每次分解时不进行下抽样,所以其表示同小波级数相比是冗余的,且图像二进小波变换的部分系数扰动不会带来重构图像的严重失真。因此,在相同的误判概率下,二进小波变换的图像去噪效果会好于小波级数变换的图像去噪效果。基于这个思想,文章从二进小波理论入手,提出一种自适应二进小波去噪模型,简称ADWD。该方法利用图像信息、噪声信息与小波系数之间的关系,采用局部自适应的方法识别噪声像素,避免了直接确定噪声门限的困难。实验结果及分析表明该方法对Gaussian噪声和Pepper噪声均有较高的信噪比,且对图像的细节有较好的保持能力。     

Geometric error analysis of an over-constrained parallel tracking mechanism using the screw theory

This paper deals with geometric error modeling and sensitivity analysis of an overconstrained parallel tracking mechanism. The main contribution is the consideration of overconstrained features that are usually ignored in previous research. The reciprocal property between a motion and a force is applied to tackle this problem in the framework of the screw theory. First of all, a nominal kinematic model of the parallel tracking mechanism is formulated. On this basis, the actual twist of the moving platform is computed through the superposition of the joint twist and geometric errors. The actuation and constrained wrenches of each limb are applied to exclude the joint displacement. After eliminating repeated errors brought by the multiplication of wrenches, a geometric error model of the parallel tracking mechanism is built. Furthermore,two sensitivity indices are defined to select essential geometric errors for future kinematic calibration. Finally, the geometric error model with minimum geometric errors is verified by simulation with SolidWorks software. Two typical poses of the parallel tracking mechanism are selected, and the differences between simulation and calculation results are very small. The results confirm the correctness and accuracy of the geometric error modeling method for over-constrained parallel mechanisms.     

经纬仪测量图像中目标缺损区域的恢复

根据电子经纬仪序列测量图像的特性,充分利用缺损帧前后数帧图像的相关信息,采用C-均值模糊聚类技术,提出了一种测量图像缺损部分恢复方法,对恢复帧前后数帧进行聚类,并剔除噪声帧,采用有效聚类中心灰度值对缺损区域进行恢复。经试验证明：此方法恢复的目标区域图像准确,灰度变化平缓,不失真。     

复杂地形下基于三视约束的景象匹配方法

 景象匹配通过将飞行器实拍图像与机载的基准图像比较而进行导航。当飞行高度较低时,由地面大起伏引起的实时图几何变形会严重影响匹配性能,造成匹配精度下降甚至出现误匹配,这一问题严重制约了现有景象匹配系统的应用范围,比如无法应用于建筑林立的城镇地区和梯田、丘陵地带。本文首先推导了小视场内无几何变形的高空竖直透视投影模型;在此基础上,依据三视约束关系,把相邻两幅下视实时图像的特征赋给该透视模型所成的像,得到消除几何变形的特征模板,再和基准图进行匹配。因此,所提方法不仅能处理由飞行器姿态引起的实时图几何变形问题,而且适用于城镇等地面存在较大起伏的地形。理论分析和仿真实验表明,该算法取得了较好的结果,具有良好的环境适应能力,实用性较强。     

风洞模型位移视频测量的相机标定

相机的制造和装配误差难以完全消除,导致相机的光学系统存在不同程度的非线性光学畸变现象,故相机标定对确保风洞模型位移视频测量的精度至关重要。针对1m。以上的台阶标定块制造成本高、维护困难,提出基于距离标尺的相机标定方法,推导包含非线性畸变模型的共线方程,建立适应中国大尺寸风洞的低成本相机标定系统,确保模型位移视频测量相机的自校正精度。2m超声速风洞的某跨大气层飞机测力试验中,采用该方法校正DALSAe相机后,各阶梯迎角测量精度以≤0．00772。（达到高速风洞测力试验迎角精度的先进指标）,因此具有实用价值。     

一种适用于曲面结构的机器人制孔误差在线补偿技术

针对工业机器人应用于飞机零部件自动钻孔时各项误差累积造成制孔精度差的问题，提出一种利用单应关系计算机器人驱动坐标三维偏差，以在线补偿机器人制孔精度的方法。首先利用外部测量设备建立机器人制孔系统中各坐标系关系；在标定阶段，通过以一定倾斜角度固联于机器人末端的相机拍摄一幅安装于制孔工作平面上与刀轴正对的平面标定板图像，并据此完成基于单应变换的手-眼关系标定；在实际制孔过程中，机器人在测距传感器及相机的辅助下，从基准孔理论坐标对应的姿态，不断调整至基准孔正上方理想位置，通过手-眼关系计算基准孔实际位置对应的机器人驱动坐标，然后根据一组基准孔的机器人三维驱动误差，计算三维驱动误差变换矩阵，据此获得这组基准孔邻域范围内各待钻孔的机器人驱动坐标补偿量，从而实现待钻孔定位误差补偿。以飞机结构实验件为对象进行了模拟制孔验证，实验结果表明，补偿前待钻孔三维综合定位误差和法向误差测量值范围分别为2.28~2.85 mm和2.09°~3.93°，平均为2.55 mm和3.30°，补偿后制孔最大误差分别不超过0.30 mm和0.21°，满足自动制孔位置精度要求。     

Effect of assisted transverse magnetic field on distortion behavior of thin-walled components in WEDM process

Machining performance of thin-walled components made by aeronautical difficult-toprocess materials is a significant issue in the aviation manufacturing industry. Although wire electric discharge machining-low speed(WEDM-LS) is one of typical non-contact machining processes without macro cutting force, which does well in removing hardness and brittleness materials via pulsed discharge at high temperature, but few researchers have studied the thermal distortion behavior leading to a considerable g...     

遥感图像的亚像素配准技术研究

高分辨遥感技术在军民领域应用广泛,图像配准技术是高分辨遥感图像的预处理技术。实际的机载和星载遥感成像仪,由于自身成像原理和成像仪平台抖动原因,出现了遥感图像亚像素偏移误差。对亚像素图像配准技术进行系统研究,对高精度亚像素配准算法进行研究分析,并对其算法进行仿真、处理,对仿真的结果进行系统分析,通过误差分析提出配准误差修正算法,修正算法对亚像素配准进行了很好的改进,达到遥感图像预处理的目的。     

基于多模态感知与融合的无人车韧性导航系统

针对野外复杂环境下的无人车自主导航需要,建立了一种基于多源融合定位、语义建图与运动规划的智能导航系统.首先,针对IMU、轮式里程计、视觉SLAM与激光雷达SLAM等测量子系统,设计了误差状态扩展卡尔曼滤波器进行融合定位.其次,基于改进的CNN语义分割网络生成环境的语义图像,与3D激光雷达点云融合,并使用最大概率更新算法构建语义3D地图.接着,在语义和几何信息投影获得可通行性代价的基础上,提出了一种语义动态窗口的局部路径规划方法.最后,将以上感知、定位与规划方法整合成完整的智能导航系统,在城市与野外典型场景的测试中,相对定位误差小于0.4％D,具备一定的韧性导航定位和智能感知规划能力.     

A toolpath strategy for improving geometric accuracy in double-sided incremental sheet forming

The double-sided incremental forming (DSIF) improved the process flexibility compared to other incremental sheet forming (ISF) processes. Despite the flexible nature, it faces the challenge of low geometric precision like ISF variants. In this work, two strategies are used to overcome this. First, a novel method is employed to determine the optimal support tool location for improving geometric precision. In this method, the toolpath oriented the tools to each other systematically in the circumferential direction. Besides, it squeezed the sheet by the same amount at the point of interest. The impacts of various support tool positions in the circumferential direction are evaluated for geometric precision. The results demonstrate that the support tool should support the master tool within 10° to its local normal in the circumferential direction to improve the geometric accuracy. Second, a two-stage process reduced the geometric error of the part by incrementally accommodating the springback error by artificially increasing the step size for the second stage. With the optimal support tool position and two-stage DSIF, the geometric precision of the part has improved significantly. The proposed method is compared to the best DSIF toolpath strategies for geometric accuracy, surface roughness, forming time, and sheet thickness fluctuations using grey relational analysis (GRA). It outperforms the other toolpath strategies including single-stage DSIF, accumulative double-sided incremental forming (ADSIF), and two-stage mixed double sided incremental forming (MDSIF). Our approach can improve geometric precision in complex parts by successfully employing the support tool and managing the springback incrementally.     

Surface Measurement Based on Instantaneous Random Illumination

This article introduces a surface measurement system which is able to take a measurement in an extremely short period of less than 2 milliseconds. It is composed of two cameras together with a specifically designed projector that projects an instantaneous random image pattern onto the objective surface. With the aid of the projected pattern, a match of image with corresponding sub-pixel is steadily established and a cluster of dense 3D points is obtained at the same time. A detailed discussion about the design and implementation of hardware is presented. A system calibration algorithm taking into account the possible geometric error of the planar calibration board is also put forward. Experiments are carried out to evaluate the system accuracy and its capability of capturing details of complex surfaces.     

激光图像融合算法

为了有效提高激光强度图像的质量,使其能够更好地与其他多源图像匹配,提出了一种激光强度图像与距离图像融合的方法.该方法首先分析了激光强度图像与距离图像不同类型像素点之间局部统计信息的差异,然后利用Ostu算法和模糊最小误差阈值算法求出强度像和距离像的局部统计信息的全局阈值和最佳阈值,最后将这些阈值作为融合参数,按照一定的融合规则,将强度像与距离像进行融合.仿真实验表明:与单纯的滤波算法相比,该融合算法在控制激光散斑噪声的同时,通过融合不同源图像的互补信息,达到提高激光强度图像的各方面性能的目的.     

伺服加工平台的综合误差建模与补偿

伺服加工平台的几何误差是影响工件加工形貌精度的重要因素。在多体系统理论的基础上,首先,运用低序体阵列描述伺服平台多体系统的拓扑结构,建立各相邻体间的齐次变换矩阵,构建其综合误差模型;其次,利用雷尼绍测量系统对平台的定位误差、直线度误差、俯仰误差、偏摆误差以及两轴之间的垂直度误差进行辨识,将辨识得到的误差值代入构建的模型得出平台几何综合误差;最后,选择凸锥面、平面、球面3种面型工件补偿试验,对误差模型进行原理性验证。研究表明,经补偿后加工所得3种面型工件表面精度均提高了25%左右,证实了提出的补偿方法可用于伺服加工平台几何误差控制。     

基于图像灰度的SSDA匹配算法

首先介绍了图像匹配的基本理论,图像的灰度失真、噪声等误差因素会影响匹配性能,并对SSDA算法进行改进,减小了计算量。为了验证算法的有效性,进行了大量仿真实验,结果表明这种方法匹配效果良好,相比于传统的图像匹配算法,计算速度更快、匹配误差更小、鲁棒性更好。     

基于改进微粒群优化算法的互信息医学图像配准

医学图像配准是寻找使两幅图像对应点达到空间位置和解剖结构上一致的过程,对于医学临床科研、诊断等方面具有非常重要的意义。基于互信息的配准方法是目前医学图像配准中无创、自动化程度高且配准精度很高的一种方法,已被广泛应用。但是由于插值赝像导致在其目标函数中存在幅值振荡现象,使用局部最优化搜索有时会终止于局部极值,得到错误的配准参数。提出针对互信息配准方法的特点,使用改进微粒群全局搜索算法,调节该算法的参数以适应不同搜索阶段,保证了最优化搜索的准确性,提高了基于互信息方法配准的成功率。     

An Analytical Expression for Correlation Accuracy

An analytical closed-form expression for the uncertainty in aligning two images is derived as a function of image area, data fidelity, scene correlation length, limiting sensor resolution length, and degree of geometrical distortion between the two images.     

一种鲁棒的红外与可见光多级景象匹配算法

针对基于特征的景象匹配方法对图像噪声和局部形变适应性差以及对特征质量依赖性强等问题,从提高图像特征描述能力入手,将具有局部光照和对比度不变性的相位一致性变换引入到红外与可见光景象匹配的特征表征中,以获得对噪声和局部形变较强的适应性。在此基础上,首先采用抗旋转的圆投影实现图像粗略匹配,然后基于Zernike矩推导出图像的互相关匹配重构函数并用来剔除错误匹配点,最后基于曲面拟合方法获得亚像素定位,从而实现图像精确与鲁棒匹配。仿真实验验证了所提算法的有效性。