A new image registration method for grey images

The proposed algorithm relies on a group of new formulas for calculating tangent slope so as to address angle feature of edge curves of image. It can utilize tangent angle features to estimate automatically and fully the rotation parameters of geometric transform and enable rough matching of images with huge rotation difference. After angle compensation, it can search for matching point sets by correlation criterion, then calculate parameters of affine transform, enable higher-precision emendation of rotation and transferring. Finally, it fulfills precise matching for images with relax-tense iteration method. Compared with the registration approach based on wavelet direction-angle features, the matching algorithm with tangent feature of image edge is more robust and realizes precise registration of various images. Furthermore, it is also helpful in graphics matching.     

A new divergence measure for medical image registration.

A new type of divergence measure for the registration of medical images is introduced that exploits the properties of the modified Bessel functions of the second kind. The properties of the proposed divergence coefficient are analysed and compared with those of the classic measures, including Kullback-Leibler, Renyi, and Iinfinity, divergences. To ensure its effectiveness and widespread applicability to any arbitrary set of data types, the performance of the new measure is analysed for Gaussian, exponential, and other advanced probability density functions. The results verify its robustness. Finally, the new divergence measure is used in the registration of CT to MR medical images to validate the improvement in registration accuracy.     

A new image registration method robust to noise

Image registration is a fundamental task in many image processing applications. In this paper, we estimate the translation, rotation, and scaling parameters between a reference image and a distorted image in order to register them. We use the ratio of means to estimate the scaling factor, the Radon transform to estimate the rotation angle, and the fast Fourier transform to estimate global spatial shifts. Experimental results show that our proposed method can estimate the transformation parameters very accurately. Furthermore, our method performs very well in noisy environments.     

适用于分段运动模式的基于链路维持概率的跨层路由协议

MANET中节点移动造成路由中断带来端到端传输性能下降,为解决这一问题提出了一种适用于分段运动模式的链路稳定概率计算方法,并在此算法的基础上设计了一种适用于分段运动模式的基于链路维持概率的跨层路由协议(LPCP).LPCP跨层结合路由层和传输层,能够兼顾端到端连接的稳定性和最小跳数.制定了"路由切换"、"数据存储转发"、"ACK再确认"等机制尽可能减少路由中断的发生及其带来的端到端传输性能下降.仿真结果表明LPCP能够有效减小路由中断带来的丢包,提高端到端传输吞吐量.     

基于合作目标与非合作目标的一体化空间配准新算法

针对移动平台雷达同时探测到合作目标与非合作目标怎样进行空间配准的问题,提出了一种一体化的空间配准算法,将基于合作目标的雷达系统偏差估计结果作为附加条件输入到基于非合作目标建立的线性方程组中,一起采用递推最小二乘法（RLS）重新估计两平台的系统偏差大小。仿真结果表明该新方法切实可行,合理地利用了合作目标信息与共同量测的非合作目标信息,提高了雷达量测系统偏差估计的准确性和最终目标航迹的配准精度,相信在多机协同探测领域具有一定的应用价值。     

Coincident bit counting-a new criterion for image registration

A similarity measure based on the number of coincident bits in multichannel images is presented. The similarity criterion incorporated in the image registration algorithm uses a coincident bit counting (CBC) method to obtain the number of matching bits between the frames of interest. The CBC method not only performs favorably compared with traditional techniques, but also renders simpler implementation in conventional computing machines. An image registration algorithm that incorporates the CBC criterion is proposed to determine the translation motion among sequences of images. The errors caused by noise, misregistration, and a combination of these two are analyzed. Some experimental studies using low-contrast coronary images from a digital angiographic sequence are discussed. The results compare favorably with those obtained by using other nonparametric methods.     

A registration framework for the comparison of mammogram sequences

In this paper, we present a two-stage algorithm for mammogram registration, the geometrical alignment of mammogram sequences. The rationale behind this paper stems from the intrinsic difficulties in comparing mammogram sequences. Mammogram comparison is a valuable tool in national breast screening programs as well as in frequent monitoring and hormone replacement therapy (HRT). The method presented in this paper aims to improve mammogram comparison by estimating the underlying geometric transformation for any mammogram sequence. It takes into consideration the various temporal changes that may occur between successive scans of the same woman and is designed to overcome the inconsistencies of mammogram image formation.     

A new method for the prediction of fade duration statistics in satellite links above 10 GHz

A model for the statistical prediction of fade durations, applicable both to scintillation and rain-induced effects, is presented in this paper. It assumes that durations longer than about 1 minute are lognormally distributed whereas shorter durations follow a power-law distribution. The model is assessed using data contained in the CCIR data bank and from measurements performed with the SIRIO and Olympus satellites. The model is tested using the same data, and the results give multiplicative errors in the range from 0.6 to 1.8 (averages) and from 0.3 to 3.0 (r.m.s.) in the estimation of the fade durations exceeded for fixed probability.     

一种HDI板对位方式简析

HDI板的对位精度问题主要涉及有通孔、盲孔与外层干膜的对位偏移问题,板边工具孔作为线路板工序生产必须的定位孔,其精度与对位方式决定线路板的生产精度。文章主要介绍一种HDI板的对位方式,重点解决HDI板通孔与盲孔的对位匹配,从而保证外层的孔环无崩孔问题。     

A comparison of methods for mammogram registration

Mammogram registration is an important technique to optimize the display of cases on a digital viewing station, and to find corresponding regions in temporal pairs of mammograms for computer-aided diagnosis algorithms. Four methods for mammogram registration were tested and results were compared. The performance of all registration methods was measured by comparing the distance between annotations of abnormalities in the previous and current view before and after registration. Registration by mutual information outperformed alignment based on nipple location, alignment based on center of mass of breast tissue, and warping.     

A new method to compute registration error from DFM type teststructures

A new method to compute registration error from the slanted die-fit-monitor (DFM)-type test structures using an on-line calibration curve has been presented. The method is suitable for use in Keithley 350, parametric measurement systems for estimation of registration error during measurement where available computing power is limited. The results obtained from this method are compared with the registration errors calculated from the standard DFM structure     

基于多图像特征的遥感图像配准新方法

针对不同视角遥感图像配准中的非刚性几何畸变造成的配准误差,文章提出了一种基于尺度不变特征变换(SIFT)特征距离和几何结构描述符的精确方法,用于解决中等地形起伏和成像视点变化条件下遥感图像的配准问题。该方法采用尺度不变特征变换和部分强度不变特征描述符提取可靠的特征点集,利用变换过程中约束几何结构的多图像特征,提出一种新的算法来估计点集之间的精确对应关系。包括特征描述符提取、基于几何结构约束的改进SIFT特征点集配准和非刚性图像变换等步骤。对不同视点的无人机图像和卫星图像的实验结果表明,该方法相对于目前几种先进方法具有更佳的配准性能。     

A new technique for determining long-term TDDB accelerationparameters of thin gate oxides

A new technique, the dual voltage versus time curve (V-t) integration technique, is presented as a much faster method to obtain time-dependent dielectric breakdown (TDDB) acceleration parameters (α and τ) of ultrathin gate oxides compared to conventional long-term constant voltage stress tests. The technique uses V-t curves measured during highly accelerated constant or ramped current injection breakdown tests. It is demonstrated that the technique yields acceleration parameters that are statistically identical to values obtained from long-term constant voltage TDDB tests. In contrast to traditional TDDB tests, the proposed technique requires over an order of magnitude less testing time, a smaller sample size, and can be used during production monitoring     

多传感器图像的二次匹配算法

针对光学图像的多光谱、多传感器图像的自动配准,提出了一种基于边缘重心的多传感器图像二次匹配算法.首先,通过边缘检测和相应的后期链接处理,提取出较为完整的图像边缘;然后,根据图像边缘的链码相关以及区域不变矩策略,寻找匹配边缘,并对已匹配边缘的重心进行一致性检验获得控制点对;最后,采用最小二乘法估计图像粗匹配变换参数.在粗匹配的基础上,根据匹配后图像共有区域内边缘的主要方向和重心,再次对图像进行细匹配,修正匹配参数以提高匹配精度.多种遥感图像数据的配准实验和对比试验证实了该自动算法具有较高的可靠性和配准精度.     

A new validation method for X-ray mammogram registration algorithms using a projection model of breast X-ray compression

Establishing spatial correspondence between features visible in X-ray mammograms obtained at different times has great potential to aid assessment and quantitation of change in the breast indicative of malignancy. The literature contains numerous nonrigid registration algorithms developed for this purpose, but existing approaches are flawed by the assumption of inappropriate 2-D transformation models and quantitative estimation of registration accuracy is limited. In this paper, we describe a novel validation method which simulates plausible mammographic compressions of the breast using a magnetic resonance imaging (MRI) derived finite element model. By projecting the resulting known 3-D displacements into 2-D and generating pseudo-mammograms from these same compressed magnetic resonance (MR) volumes, we can generate convincing images with known 2-D displacements with which to validate a registration algorithm. We illustrate this approach by computing the accuracy for two conventional nonrigid 2-D registration algorithms applied to mammographic test images generated from three patient MR datasets. We show that the accuracy of these algorithms is close to the best achievable using a 2-D one-to-one correspondence model but that new algorithms incorporating more representative transformation models are required to achieve sufficiently accurate registrations for this application.     

A new bio-inspired metric based on eye movement data for classifying ASD and typically developing children

In this paper, we propose a new bio-inspired metric for classifying autism spectrum disorder (ASD) children and typically developed (TD) children. The model used in the Saliency4ASD Grand Challenge at ICME 2019 uses linear regression and prior probability to process distance and time data respectively. Unfortunately, this model performs unsatisfactorily because the visual attention characteristics of ASD and TD children are similar under certain circumstances. Therefore, we screen stimulus materials to select these with significant differences between eye movement distribution of ASD and TD children. We calculate the SSIM value of the ASD and TD data of each picture and conduct the subjective experiments to classify the stimulus materials into two categories: the images with the similar attention map for ASD and TD children; and the images with the dissimilar attention map for ASD and TD children. Owing to the biological property of eye, a viewing angle will be formed when people are observing a picture. Meanwhile, gazing at one point of longer time means more attention. Thus, we pick the point of the longest fixation time for each data group and extract the patch centered on this point. Three point-add strategies are afterward utilized to add points on this patch. Subsequently, a new bio-inspired metric based on graph theory is developed. Experimental results show that the new model outperforms our previous model with a classification accuracy of 72.3%.     

A protocol for evaluation of similarity measures for rigid registration

The accuracy and robustness of a registration method depend on a number of factors, such as imaging modality, image content and image degrading effects, the class of spatial transformation used for registration, similarity measure, optimization, and numerous implementation details. The complex interdependence of these factors makes the assessment of the influence of a particular factor on registration difficult, although it is often desirable to have some estimate of such influences prior to registration. The similarity measure used to create the cost function is one of the factors that most influences the quality of registration. Traditionally, limited information on the behavior of a similarity measure is obtained either by studying the quality of the final registration or by drawing plots of similarity measure values obtained by translating or rotating one image relative to the "gold standard." In this paper, we present a protocol for a more thorough, optimization-independent, and systematic statistical evaluation of similarity measures. This protocol estimates a similarity measure's capture range, the number, location and extent of local optima, and the accuracy and distinctiveness of the global optimum. To show that the proposed evaluation protocol is viable, we have conducted several experiments with nine similarity measures and real computed tomography and magnetic resonance (MR) images of a spine phantom, MR brain images, and MR and positron emission tomography brain images, for which "gold standard" registrations were available. We have also studied the impact of histogram bin size on the behavior of nine similarity measures. The proposed evaluation protocol is useful for selecting the best similarity measure and corresponding optimization method for a particular application, as well as for studying the influence of sampling, interpolation, histogram bin size, partial image overlap, and image degradation, such as noise, intensity inhomogeneity, and geometrical distortions on the behavior of a similarity measure.     

点云配准中的初始配准研究

针对精确配准容易陷入局部最优的问题,文章提出结合初始配准与精确配准相结合,更好地完成点云数据集的精确配准。通过实验表明,在两个点云数据集旋转角度相差较大时,使用传统的精确配准算法会陷入局部最优,导致配准失败;同时,实现了FPFH算法和4PCS算法,在原始点云数据旋转角度偏差较大时能够完成点云数据的初始配准,更有利于精确配准的实现。     

A generalized divergence measure for robust image registration

Entropy-based divergence measures have shown promising results in many areas of engineering and image processing. We define a new generalized divergence measure, namely, the Jensen-Renyi (1996, 1976) divergence. Some properties such as convexity and its upper bound are derived. Based on the Jensen-Renyi divergence, we propose a new approach to the problem of image registration. Some appealing advantages of registration by Jensen-Renyi divergence are illustrated, and its connections to mutual information-based registration techniques are analyzed. As the key focus of this paper, we apply Jensen-Renyi divergence for inverse synthetic aperture radar (ISAR) image registration. The goal is to estimate the target motion during the imaging time. Our approach applies Jensen-Renyi divergence to measure the statistical dependence between consecutive ISAR image frames, which would be maximal if the images are geometrically aligned. Simulation results demonstrate that the proposed method is efficient and effective.