一种基于图像序列和外部平移数据的摄像机自定标方法

摄像机标定是从二维图像获取三维信息必不可少的步骤。本文提出了一种新的基于图像序列的自定标方法。通过控制摄像机平台作3次平移运动（其中任意2次均不在同一平面上）且不需要读取运动平台的平移数据，即可线性地标定摄像机的内参数。大量的模拟实验表明该算法精度高和鲁棒性强。     

离焦模糊图像的盲复原算法

针对离焦模糊图像,提出了一种盲复原算法。该算法首先利用Hough变换检测出离焦图像中的直线边缘,然后基于图像的空域统计特性和修正的Grubbs检验法,定位出阶跃或近似阶跃直线边缘,在此基础上自适应计算出线扩散函数,最后利用线扩散函数求取离焦模糊半径,进而用Wiener滤波完成了图像的复原。实验结果表明,对真实的离焦模糊图像,该算法能够准确地检测和定位出阶跃或近似阶跃边缘,提高离焦模糊半径的鉴别精度和图像的复原效果,已在实际刑侦取证工作中获得较为成功的应用。     

顺序滤波器的相对误差准则和最佳估计

在相对误差意义下定义了针对一种简单的顺序滤波器的两种相对误差准则,并在该准则基础上得到了相应的最佳估值.该结果进一步完善了非线性滤波器研究的理论体系,为进一步开展该领域的研究提供了一种可供借鉴的新思路和方法.     

重建误差最优化的运动捕获数据关键帧提取

为了实现运动捕获数据的实时压缩,提出一种重建误差最优化的关键帧提取方法.定义重建误差作为关键帧有效性的度量标准;利用帧消减算法得到各消减帧的重建误差,并根据误差大小对消减帧进行排序;最后以消减帧排序为基础计算出重建误差曲线,并依据曲线确定最优压缩率来提取出相应数量的关键帧.实验结果表明,该方法能够高效地从运动捕获数据中提取出具有最优重建误差的关键帧,较好地满足了数据实时压缩的需要.     

塑料齿轮传动误差试验机数据采集系统设计

塑料齿轮传动误差试验机是一种新型齿轮测量仪器,针对塑料齿轮传动误差试验机对数据采集系统高速度、多种类、并行采集的技术要求,设计了一款基于Zynq7000的数据采集系统。通过Zynq7000的PL部分实现多路光栅信号的同步采集,设计状态机驱动一个16位精度的ADC芯片ADS8584S采集模拟信号,把采集到的结果写入FIFO中,通过AXI和DMA控制器把数据存储到DDR3中,利用LWIP协议栈实现了数据采集系统和上位机之间以太网通信。经初步验证,该系统可以实现光栅信号和模拟信号采集、电机控制和频率测量等功能,光栅信号采集频率可以达到10 MHz,模信号采集精度可以达到0.06%。系统能够满足试验机数据采集要求,也可以用于其他仪器光栅信号高速采集,采用网络通信,为以后仪器智能测量、远程测量奠定了基础,具有很好的应用前景。     

基于虚拟仪器的数据采集板卡校准技术研究

针对功能的多样性、精度与集成度的提高以及校准软件的缺乏等造成板卡校准工作不能正确展开的问题,以4款板卡的校准为例,探讨了板卡校准的步骤和校准的软硬件需求.在分析仪器控制技术、校准流程和校准任务的基础上,提出了“校准引擎+校准数据库+校准插件”的新校准软件开发架构,并介绍了其组成和实现中采用的关键技术.此校准软件已成功应用到工程中,为板卡校准工作提供了有效的技术手段.     

高能X射线CT动态校准及数据采集方法

本文介绍一种高能X射线CT动态校准及数据采集方法。给出了系统的整体构成、实现及工作原理,并进一步阐述了动态校准及数据采集的具体流程和步骤。通过该方法可以在降低噪声等的干扰的同时,有效提高数据采集效率,具有很高的实际应用价值。     

图像的盲解卷积恢复

通常图像恢复方法均在成像系统的点扩展函数PSF已知下进行,实际上它通常是未知的。在PSF未知的情况下,盲解卷积是实现图像恢复的有效方法。该算法优点是,在对失真情况毫无先验知识的情况下,仍能实现对模糊图像的恢复操作。给出了利用MATLAB实现的图像恢复实例,并对恢复图像的失真情况做了改善。     

Regularized Bundle-Adjustment to Model Heads from Image Sequences without Calibration Data

We address the structure-from-motion problem in the context of head modeling from video sequences for which calibration data is not available. This task is made challenging by the fact that correspondences are difficult to establish due to lack of texture and that a quasi-euclidean representation is required for realism.We have developed an approach based on regularized bundle-adjustment. It takes advantage of our rough knowledge of the head's shape, in the form of a generic face model. It allows us to recover relative head-motion and epipolar geometry accurately and consistently enough to exploit a previously-developed stereo-based approach to head modeling. In this way, complete and realistic head models can be acquired with a cheap and entirely passive sensor, such as an ordinary video camera, with minimal manual intervention.We chose to demonstrate and evaluate our technique mainly in the context of head-modeling. We do so because it is the application for which all the tools required to perform the complete reconstruction are available to us. We will, however, argue that the approach is generic and could be applied to other tasks, such as body modeling, for which generic facetized models exist.     

Optimal Filters with Multiresolution Apertures

This paper presents an extension to the recently introduced class of nonlinear filters known as Aperture Filters. By taking a multiresolution approach, it can be shown that more accurate filtering results (in terms of mean absolute error) may be achieved compared to the standard aperture filter given the same size of training set. Most optimisation techniques for nonlinear filters require a knowledge of the conditional probabilities of the output. These probabilities are estimated from observations of a representative training set. As the size of the training set is related to the number of input combinations of the filter, it increases very rapidly as the number of input variables increases. It can be impossibly large for all but the simplest binary filters. In order to design nonlinear filters of practical use, it is necessary to limit the size of the search space i.e. the number of possible filters (and hence the training set size) by the application of filter constraints. Filter constraints take several different forms, the most general of which is the window constraint where the output filter value is estimated from only a limited range of input variables.Aperture filters comprise a special case of nonlinear filters in which the input window is limited not only in its domain (or duration) but also in its amplitude. The reduced range of input signal leads directly to a reduction in the size of training set required to produce accurate output estimates. However in order to solve complex filtering problems, it is necessary for the aperture to be sufficiently large so as to observe enough of the signal to estimate its output accurately.In this paper it is shown how the input range of the aperture may be expanded without increasing the size of the search space by adopting a multiresolution approach. The constraint applied in this case is the resolution constraint. This paper presents both theoretical and practical results to demonstrate and quantify the improvement.     

基于最小二乘估计的数据采集通道标定方法

由于数据采集通道受外界环境因素的影响,A/D转换器输出数值与输入模拟信号之间对应的理想数学关系式不再成立;为了得出输入信号与A/D输出数值之间的对应关系,在分析电路所采用器件电气特性的基础上,建立了数据采集通道的标定模型,并给出了基于最小二乘原则的模型参数估计方法;通过对直流电压信号的直流准确度和对正弦交流信号有效值的测试,证明了该模型的正确性和可行性.     

机载偏振成像系统的数据采集

介绍了机载偏振成像系统中的3路CCD相机同步控制PCI图像采集卡的工作原理，详细阐述了图像数据采集系统的硬件结构设计和图像采集卡的驱动编程方法，介绍了为实现高速图像数据传输需求所采用的DMA、多线程和双缓冲技术。本系统实现了Windows2000操作系统下同步控制3路CCD相机，以20MB／s数据率完成高速数据的传输与存储。     

一个高速图象数据采集系统

介绍视频图象印相机中所采用的高速图象数据采集系统。该系统用来完成图象信号的视频处理，高速数据采集，并将采集到的高速图象数据流存入图象存贮阵列。系统主要由梳状滤波器、视频放大、同步分离、模数变换、图象存贮器以及有关的微机控制部分组成。文中着重讨论了快速Ａ／Ｄ变换，采用象素缓冲技术的快速存贮以及整个系统的同步和控制等关键技术。实验结果表明，系统能达到上述每点采集时间的要求，对于图片的６４０×６０８空间分辨率要求，一幅图象的采集时间约为３１．５ｍｓ。     

宽频带梯度水听器校准系统中的数据采集与处理

研制了适用于宽频带梯度水听器校准系统的数据采集板及预处理差分放大器，并且在这些硬件基础上研究设计了专门的软件程序，实现实量系统控制及流程控制，并且可以在多信息界面提示下进行相应的手动操作。本系统具有数据采集、数据处理、图形控制以及图形和数据表格方式输出结果等功能。     

Novel Deconvolution of Noisy Gaussian Filters with a Modified Hermite Expansion

We have shown (J. Appl. Phys., 1990, 1415-1420) that deconvolving an image which was blurred by a Gaussian filter is equivalent to antidiffusing the image for an appropriate duration of time. However, the antidiffusion algorithm used to show this, based on backward integration of the diffusion equation, is extremely sensitive to noise with numerical errors increasing exponentially with time. Thus, an extremely high signal to noise ratio is required for reconstruction of a blurred image via antidiffusion. In this paper, we introduce a new antidiffusion algorithm which is substantially more robust with respect to noise. This is because each functional component in the series of the reconstructed image is obtained analytically from a corresponding component of the blurred image. We show that the algorithm yields accurate reconstructions of Gaussian-smeared signals and images with extremely low signal to noise ratios.     

基于深度反卷积神经网络的图像超分辨率算法

图像超分辨率一直是底层视觉领域的研究热点,现有基于卷积神经网络的方法直接利用传统网络模型,未对图像超分辨率属于回归问题这一本质进行优化,其网络学习能力较弱,训练时间较长,重建图像的质量仍有提升空间。针对这些问题,本文提出了基于深度反卷积神经网络的图像超分辨率算法,该算法利用反卷积层对低分辨率图像进行上采样处理,再经深度映射消除由反卷积层造成的噪声和伪影现象,使用残差学习降低网络复杂度,同时避免了因网络过深导致的网络退化问题。在Set5、Set14等测试集中,本文算法的PSNR、SSIM、IFC三项评价指标皆优于FSRCNN,重建图像的视觉效果同样验证了本文算法出色的性能。     

Big Data Analytics with Optimal Deep Learning Model for Medical Image Classification

In recent years, huge volumes of healthcare data are getting generated in various forms. The advancements made in medical imaging are tremendous owing to which biomedical image acquisition has become easier and quicker. Due to such massive generation of big data, the utilization of new methods based on Big Data Analytics (BDA), Machine Learning (ML), and Artificial Intelligence (AI) have become essential. In this aspect, the current research work develops a new Big Data Analytics with Cat Swarm Optimization based deep Learning (BDA-CSODL) technique for medical image classification on Apache Spark environment. The aim of the proposed BDA-CSODL technique is to classify the medical images and diagnose the disease accurately. BDA-CSODL technique involves different stages of operations such as preprocessing, segmentation, feature extraction, and classification. In addition, BDA-CSODL technique also follows multi-level thresholding-based image segmentation approach for the detection of infected regions in medical image. Moreover, a deep convolutional neural network-based Inception v3 method is utilized in this study as feature extractor. Stochastic Gradient Descent (SGD) model is used for parameter tuning process. Furthermore, CSO with Long Short-Term Memory (CSO-LSTM) model is employed as a classification model to determine the appropriate class labels to it. Both SGD and CSO design approaches help in improving the overall image classification performance of the proposed BDA-CSODL technique. A wide range of simulations was conducted on benchmark medical image datasets and the comprehensive comparative results demonstrate the supremacy of the proposed BDA-CSODL technique under different measures.     

并行数据采集系统中非均匀采样引起的误差分析

分析了并行数据采集系统中非均匀采样引起的误差.在给出误差平均功率表达式的基础上,推导出正弦信号输入时的信噪比近似计算公式,并对影响信噪比的各种因素依次进行讨论.进一步对线性调频信号输入的情况进行研究,根据信噪比计算公式给出了选取各参数的约束条件.仿真结果验证了信噪比计算公式的正确性.     

基于PSoC的圆度误差数据采集系统

详细论述了以嵌入式片上系统PSoC为核心的圆度误差数据采集处理系统的软硬件设计.该系统灵活运用PSoC片上系统内部集成的定时模块、计数模块和全双工模块,以及外部中断和I/O等片上资源,通过使用定时模块来控制圆度误差信号的采样频率;采用计数模块与全双工模块相结合的方法来实现下位MCU同PC的串口通信,完成人机交互;同时,采用光电传感器来触发MCU的外部中断,控制测量的起始点,避免了不封闭测量和重复测量的出现.     

Multiresolution Analysis for Optimal Binary Filters

The performance of a designed digital filter is measured by the sum of the errors of the optimal filter and the estimation error. Viewing an image at a high resolution results in optimal filters having smaller errors than at lower resolutions; however, higher resolutions bring increased estimation error. Hence, choosing an appropriate resolution for filter design is important. The present paper provides expressions for both the error of the optimal filter and the design error for estimating optimal filters in a pyramidal multiresolution framework. The analysis is facilitated by a general characterization of suitable sequences of resolution-constraint mappings. The error expressions are generated from resolution to resolution in a telescoping manner. To take advantage of data at all resolutions, one can use a hybrid multiresolution design to arrive at a multiresolution filter. A sequence of filters is designed using data at increasing resolutions, each filter serves as a prior filter for the next, and the last filter is taken as the designed filter. The value of the multiresolution filter at a given observation is based on the highest resolution at which conditioning by the observation is considered significant.