景像匹配算法的性能评估方法研究

景像匹配算法性能评估的目的是建立匹配算法的性能测试规范，以利于景像图的选择和算法的改进。文中研究了适合于性能评估过程的基准图和实时图选择准则，景像匹配算法性能评估所采用的测试试验方法以及测试结果的统计分析方法，并在此基础上构造一个景像匹配算法性能评估软件系统。     

基于多元Logistic回归分析的匹配算法性能评估

在实际应用中,景像匹配算法的性能受旋转、缩放、平移等多种因素影响.选取适当的图像性能指标和算法性能指标作为输入和输出,利用多元Logistic回归模型建立两者之间的联系,通过回归分析评估匹配算法性能优劣.该方法能够综合多种影响因素评估算法性能,并确定各因素与算法性能指标的相关程度,为算法应用及改进提供依据.通过多元回归分析对两种匹配算法的性能进行了定量评估与比较,验证了评估模型的有效性.     

97091光电技术学术交流及技术研讨会部分论文摘要

下视景像匹配算法性能评估的研究金善良梁文宝（航天工业总公司三院三部北京100074）文章采用试验分析的方法评估下视录像匹配算法的性能。首先，详细地分析了下视景像匹配算法处理对象——卫片航片图像的差异，建立了与匹配性能有关的地面景像统计特性的描述方法，研究了用于匹配算法评估的景像生成方法，探讨了算法性能测试和代表性图像样本选择的途径，最终设计了下视景像匹配算法的评估系统。机载扫描激光测距地面分辨力分析胡以华魏庆农章立民（中国科学院安徽光学精密机械研究所合肥230031）文章分五个部分对水平分辨力的有关问题进行…     

下视景像匹配算法性能评估

下视景像匹配技术是项自主定位技术，良好的景像匹配算法是保证下视景像匹配系统性能的关键，如何评价下视景像匹配算法的性能是当前急需探讨的问题。文章分析了现有的评估方法及其缺陷，并对试验分析的方法进行了研究，设计了下视景像匹配算法的评估系统。     

智能地物景像匹配定位技术研究

文中研究了智能地物景像匹配定位系统的基本原理,从模式识别的观点出发提出了地物景像匹配算法的数学模型,并讨论了影响系统性能的有关因素.最后用计算机仿真验证了归一化积相关算法在智能地物景像匹配定位系统中的可用性.     

基于模糊信息理论的景像匹配方法

传统的景像匹配方法是基于图像统计信息，但实际的图像带有许多模糊信息，文章基于模糊贴近度的概念提出了一种模糊匹配算法，由于模糊集理论可以较好地描述图像的模糊信息，因此基于模糊贴近度的景像匹配方法具有较好的匹配性能。     

一种多传感器融合的景像匹配算法

为提高景像匹配算法的匹配概率和匹配精度,以多传感器最大属性数据融合方法为基础,分析了光学与雷达传感器获取图像的特性,提出了使用光学和雷达传感器进行融合相关的新匹配算法,实现了这两种传感器的优势互补,大大加强了景像匹配算法的抗干扰能力,有效提高了匹配概率和匹配精度。仿真证明,该方法比较成功。     

基于灰度组合矩阵的景像匹配算法

介绍了景像匹配算法的定义和分类.在分析了现有的灰度匹配算法的基础上,提出了基于灰度组合矩阵的匹配算法,该算法具有一般的灰度匹配算法的优点:抗噪声能力强,具有一定的抗几何畸变的能力.同时,该算法具有很强的抗灰度变换的能力,甚至可以抗灰度反转.大量实验表明,该算法具有比传统的NCC算法更优越的性能.     

采用循环相位相关的LPT景像匹配算法

景像匹配辅助惯性组合导航系统中的景像匹配算法必须同时具有克服旋转和小尺度变换的能力。通过分析发现,对数极坐标变换(LPT)能将它们分别转换为子变换图之间的循环、线性平移关系。鉴于经典的相位相关法检测的是图像间的线性平移,提出了一种改进相位相关法来检测循环平移,最终利用LPT变换和新相关法实现了景像匹配算法。仿真分析表明,当存在任意的旋转、小范围的尺度变化和噪声畸变时,本文算法匹配精度达到93%以上,时间在1.3s以内,基本满足了景像匹配辅助导航系统对图像匹配算法准确性和实时性的要求。     

动态景像匹配导航差分滤波估计定位融合算法

为了改善景像匹配导航系统的可靠性及匹配精度,提出了一种基于位置差分滤波与最小二乘估计的动态景像匹配定位融合算法。分析了动态景像匹配导航定位融合的基本问题;针对匹配位置序列的误差特性,设计了位置差分滤波方法,用于剔除匹配位置序列中的误匹配点;采用最小二乘估计对匹配位置序列进行最优参数估计,进而对误匹配点进行估计并实现了匹配位置预测;给出了定位融合算法的基本原理及实现框图。仿真实验结果验证了该算法在消除误匹配点及减小随机匹配误差干扰方面的有效性、实用性。     

Standardized evaluation methodology for 2-D-3-D registration

In the past few years, a number of two-dimensional (2-D) to three-dimensional (3-D) (2-D-3-D) registration algorithms have been introduced. However, these methods have been developed and evaluated for specific applications, and have not been directly compared. Understanding and evaluating their performance is therefore an open and important issue. To address this challenge we introduce a standardized evaluation methodology, which can be used for all types of 2-D-3-D registration methods and for different applications and anatomies. Our evaluation methodology uses the calibrated geometry of a 3-D rotational X-ray (3DRX) imaging system (Philips Medical Systems, Best, The Netherlands) in combination with image-based 3-D-3-D registration for attaining a highly accurate gold standard for 2-D X-ray to 3-D MR/CT/3DRX registration. Furthermore, we propose standardized starting positions and failure criteria to allow future researchers to directly compare their methods. As an illustration, the proposed methodology has been used to evaluate the performance of two 2-D-3-D registration techniques, viz. a gradient-based and an intensity-based method, for images of the spine. The data and gold standard transformations are available on the internet (http://www.isi.uu.nl/Research/Databases/).     

Improved labeling of subcortical brain structures in atlas-based segmentation of magnetic resonance images

Precise labeling of subcortical structures plays a key role in functional neurosurgical applications. Labels from an atlas image are propagated to a patient image using atlas-based segmentation. Atlas-based segmentation is highly dependent on the registration framework used to guide the atlas label propagation. This paper focuses on atlas-based segmentation of subcortical brain structures and the effect of different registration methods on the generated subcortical labels. A single-step and three two-step registration methods appearing in the literature based on affine and deformable registration algorithms in the ANTS and FSL algorithms are considered. Experiments are carried out with two atlas databases of IBSR and LPBA40. Six segmentation metrics consisting of Dice overlap, relative volume error, false positive, false negative, surface distance, and spatial extent are used for evaluation. Segmentation results are reported individually and as averages for nine subcortical brain structures. Based on two statistical tests, the results are ranked. In general, among four different registration strategies investigated in this paper, a two-step registration consisting of an initial affine registration followed by a deformable registration applied to subcortical structures provides superior segmentation outcomes. This method can be used to provide an improved labeling of the subcortical brain structures in MRIs for different applications.     

Fundamental performance limits in image registration

The task of image registration is fundamental in image processing. It often is a critical preprocessing step to many modern image processing and computer vision tasks, and many algorithms and techniques have been proposed to address the registration problem. Often, the performances of these techniques have been presented using a variety of relative measures comparing different estimators, leaving open the critical question of overall optimality. In this paper, we present the fundamental performance limits for the problem of image registration as derived from the Cramer-Rao inequality. We compare the experimental performance of several popular methods with respect to this performance bound, and explain the fundamental tradeoff between variance and bias inherent to the problem of image registration. In particular, we derive and explore the bias of the popular gradient-based estimator showing how widely used multiscale methods for improving performance can be explained with this bias expression. Finally, we present experimental simulations showing the general rule-of-thumb performance limits for gradient-based image registration techniques.     

Fusion of thermal and visible cameras for the application of pedestrian detection

In this paper, we propose methods to calibrate visible and thermal cameras and register their images in the application of pedestrian detection. We calibrate the camera using a checkerboard pattern mounted on a heated rig. We implement the image registration using three different approaches. In the first approach, we use the camera calibration information to generate control points from the checkerboard pattern. These control points are then used to register the images. In the second approach, we generate trajectory points for image registration using an external illuminated object. In the third approach, we achieve the registration through face tracking without the aid of any external object. The particle swarm optimization algorithm performs the image registration using the generated control and trajectory points, observed in both the cameras. We demonstrate the advantages of fusing the thermal and visible camera within a pedestrian detection algorithm. We evaluate the proposed registration algorithms and perform a comparison with baseline algorithms, i.e. genetic and simulated annealing algorithms. Additionally, we also perform a detailed parameter evaluation of the particle swarm optimization algorithm. The experimental results demonstrate the accuracy of the proposed algorithm and the advantages of thermal-visible camera fusion.     

Evaluation of registration methods on thoracic CT: the EMPIRE10 challenge

EMPIRE10 (Evaluation of Methods for Pulmonary Image REgistration 2010) is a public platform for fair and meaningful comparison of registration algorithms which are applied to a database of intrapatient thoracic CT image pairs. Evaluation of nonrigid registration techniques is a nontrivial task. This is compounded by the fact that researchers typically test only on their own data, which varies widely. For this reason, reliable assessment and comparison of different registration algorithms has been virtually impossible in the past. In this work we present the results of the launch phase of EMPIRE10, which comprised the comprehensive evaluation and comparison of 20 individual algorithms from leading academic and industrial research groups. All algorithms are applied to the same set of 30 thoracic CT pairs. Algorithm settings and parameters are chosen by researchers expert in the configuration of their own method and the evaluation is independent, using the same criteria for all participants. All results are published on the EMPIRE10 website (http://empire10.isi.uu.nl). The challenge remains ongoing and open to new participants. Full results from 24 algorithms have been published at the time of writing. This paper details the organization of the challenge, the data and evaluation methods and the outcome of the initial launch with 20 algorithms. The gain in knowledge and future work are discussed.     

多传感器误差配准研究

多传感器组网信息融合时,需要对组网中各传感器系统误差进行估计和补偿,以消除各传感器系统误差对融合性能的影响.研究了雷达组网系统误差配准模型,并对最小二乘算法(LS)、广义最小二乘算法(GLS)、递推最小二乘算法(RLS)、修正EX算法等误差配准算法进行对比分析,同时给出了扩维配准模型用于解决多传感器组网配准问题,针对实...     

配准误差下的多基地雷达目标检测算法

在多基地雷达系统中,即使进行了空间配准处理,也很难实现完美的空间配准。该文研究了分布式MIMO雷达系统存在配准误差时的目标检测问题。根据是否利用已知先验配准误差信息对目标位置信息进行估计,给出了MAP-GLRT和ML-GLRT两种检测器。由于MAP-GLRT检测器利用了先验信息,因此其检测性能优于ML-GLRT检测器。在配准误差条件下,两种检测器的性能要优于传统的融合检测算法。通过仿真实验验证了所提算法的有效性。     

基于深度学习的电力设备红外与可见光图像配准

针对现有电力设备红外与可见光图像配准难度大、配准时间长等问题,提出一种基于深度学习的电力设备红外与可见光图像配准融合的方法。本文将特征提取与特征匹配联合在深度学习框架中,直接学习图像块对与匹配标签之间的映射关系,用于后续的配准。此外为了缓解训练时红外图像样本不足的问题,提出一种利用红外图像及其变换图像学习映射函数的自学习方法,同时采用迁移学习来减少训练时间,加速网络框架。实验结果表明：本文方法与其他4种配准算法相比性能指标均有显著提升,本文平均准确率为89.909,同其余4种算法相比分别提高了2.31%、3.36%、2.67%、0.82%,本文平均RMSE(Root Mean Square Error)为2.521,同其余4种配准算法相比分别降低了14.68%、15.24%、4.90%、1.04%,算法平均用时为5.625 s,较其余4种算法分别降低了5.57%、6.82%、2.45%、1.75%,有效提高了电力设备红外与红外可见光图像配准的效率。     

Nonrigid 2D/3D registration of coronary artery models with live fluoroscopy for guidance of cardiac interventions

A 2D/3D nonrigid registration method is proposed that brings a 3D centerline model of the coronary arteries into correspondence with bi-plane fluoroscopic angiograms. The registered model is overlaid on top of interventional angiograms to provide surgical assistance during image-guided chronic total occlusion procedures, thereby reducing the uncertainty inherent in 2D interventional images. The proposed methodology is divided into two parts: global structural alignment and local nonrigid registration. In both cases, vessel centerlines are automatically extracted from the 2D fluoroscopic images, and serve as the basis for the alignment and registration algorithms. In the first part, an energy minimization method is used to estimate a global affine transformation that aligns the centerline with the angiograms. The performance of nine general purpose optimizers has been assessed for this problem, and detailed results are presented. In the second part, a fully nonrigid registration method is proposed and used to compensate for any local shape discrepancy. This method is based on a variational framework, and uses a simultaneous matching and reconstruction process to compute a nonrigid registration. With a typical run time of less than 3 s, the algorithms are fast enough for interactive applications. Experiments on five different subjects are presented and show promising results.