不完全投影数据图像重建的ART算法研究

ART算法是一种经典的图像重建方法,适合于不完全投影数据重建。为了提高重建速度,通常对权因子进行简化,其结果是在重建图像中普遍存在椒盐噪声。提出了一种改善重建质量的方法,在每次迭代后对重建图像进行有选择的平滑,将平滑结果作为下一次迭代的初值,其特点是将图像处理和图像重建相结合。仿真实验表明该方法非常有效,不但提高了重建质量,而且克服了利用Box模板进行平滑所造成的图像模糊现象。     

Accurate spatio-temporal reconstruction of missing data in dynamic scenes

In this paper, the accurate method for texture reconstruction with non-desirable moving objects into dynamic scenes is proposed. This task is concerned to editor off-line functions, and the main criteria are the accuracy and visibility of the reconstructed results. The method is based on a spatio-temporal analysis and includes two stages. The first stage uses a feature points tracking to locate the rigid objects accurately under the assumption of their affine motion model. The second stage involves the accurate reconstruction of video sequence based on texture maps of smoothness, structural properties, and isotropy. These parameters are estimated by three separate neural networks of a back propagation. The background reconstruction is realized by a tile method using a single texton, a line, or a field of textons. The proposed technique was tested into reconstructed regions with a frame area up to 8–20%. The experimental results demonstrate more accurate inpainting owing to the improved motion estimations and the modified texture parameters.     

1种基于遗失数据重构的软测量方法

提出1种遗失数据重构思想下的软测量方法:先采用主元分析(PCA)离线建立所有变量(包括难测变量)的主元模型,实际应用时,将实时的难测变量看作遗失数据,通过遗失数据重构方法估计出难测变量,增加了软测量方法的灵活性.更进一步,在重构遗失数据时,使用马氏距离取代欧几里德距离作为指标,更准确地反映了过程变量之间的相关关系,由此...     

Capturing tree crown formation through implicit surface reconstruction using airborne lidar data

Forest structure data derived from lidar is being used in forest science and management for inventory analysis, biomass estimation, and wildlife habitat analysis. Regression analysis dominated previous approaches to the derivation of tree stem and crown parameters from lidar. The regression model for tree parameters is locally applied based on vertical lidar point density, the tree species involved, and stand structure in the specific research area. The results of this approach, therefore, are location-specific, limiting its applicability to other areas. For a more widely applicable approach to derive tree parameters, we developed an innovative method called ‘wrapped surface reconstruction’ that employs radial basis functions and an isosurface. Utilizing computer graphics, we capture the exact shape of an irregular tree crown of various tree species based on the lidar point cloud and visualize their exact crown formation in three-dimensional space. To validate the tree parameters given by our wrapped surface approach, survey-grade equipment (a total station) was used to measure the crown shape. Four vantage points were established for each of 55 trees to capture whole-tree crown profiles georeferenced with post-processed differential GPS points. The observed tree profiles were linearly interpolated to estimate crown volume. These fieldwork-generated profiles were compared with the wrapped surface to assess goodness of fit. For coniferous trees, the following tree crown parameters derived by the wrapped surface method were highly correlated (p < 0.05) with the total station-derived measurements: tree height (R² = 0.95), crown width (R² = 0.80), live crown base (R² = 0.92), height of the lowest branch (R² = 0.72), and crown volume (R² = 0.84). For deciduous trees, wrapped surface-derived parameters of tree height (R² = 0.96), crown width (R² = 0.75), live crown base (R² = 0.53), height of the lowest branch (R² = 0.51), and crown volume (R² = 0.89) were correlated with the total station-derived measurements. The wrapped surface technique is less susceptible to errors in estimation of tree parameters because of exact interpolation using the radial basis functions. The effect of diminished energy return causes the low correlation for lowest branches in deciduous trees (R² = 0.51), even though leaf-off lidar data was used. The wrapped surface provides fast and automated detection of micro-scale tree parameters for specific applications in areas such as tree physiology, fire modeling, and forest inventory.     

基于重构技术工业故障诊断OPC数据平台的设计

当今日益飞速发展的自动化控制技术使得工厂的自动化程度越来越高,然而带来的问题就是一旦检测设备发生故障,会带来控制系统的崩溃,轻则带来经济损失重则造成工业事故.本文就工业故障诊断技术利用OPC(OLE for Processing Control)技术设计1种容错控制数据平台来解决这一问题.通过OPC服务获得工业现场数据,利用故障诊断技术对工业数据进行诊断,检测辨识故障传感器,隔离故障传感器,采用已知数据回归法(Known Data Regression)重构故障数据,作为上层控制软件的输入从而可以使控制系统稳定运行,故障监控软件给操作人员发出检修信号.实验室仿真结果表明该平台可以稳定运行,这为故障诊断和容错技术实际工程应用奠定了基础.     

逆向工程中测量数据曲面重构技术研究

文中首先介绍了自由曲面的描述方式,并提出了一种对点云数据进行NURBS曲面重建的改进算法。该算法通过对目标函数的修正及迭代处理,将测点数据的有理B样条曲线拟合问题转化为一单变量的优化问题。从而容易求得控制点三维坐标、权值、各测点所对应的参数值及最优控制点的数目,进而有效地压缩测量点云数据量。该方法克服了现有方法存在的需要求解多变量的非线性优化问题,不能对最优控制点数目进行求取等缺陷。实践表明,文中提出的方法是行之有效的。     

基于概率PCA过程监控中遗失数据的重构

在实际工业过程中,PCA常常被用于数据重构。但是相比于概率PCA(PPCA),PCA无论在建模上还是在统计监控指标上都存在一些缺陷。基于此,本文提出一种基于PPCA的遗失数据重构方法。通过使样本数据点与其在PPCA模型上的投影点之问的距离最小,该方法能够有效地进行数据重构。此外,还分析了使样本数据白化值最小的数据重构方法。在田纳西-伊斯曼过程中的应用验证了其有效性。     

探测器倾斜的锥束FDK修正重建算法

在锥束工业CT系统中,通常要求射线源焦点、物体旋转中心、平板探测器中心三点在一条直线上,与平板探测器垂直。由于机械的定位误差,探测器定位后会出现一定角度的倾斜现象,如果直接利用平板探测器倾斜后采集的投影数据进行重建,重建结果将会出现偏离,产生伪影。针对该问题,利用系统标定的方法测得探测器倾斜角度,利用机械校正对其进行粗调,利用提出的基于探测器倾斜的FDK修正算法进行微调,经过两次校正可以有效抑制由于平板探测器倾斜造成的伪影,通过实验验证了该方法的可行性。     

Trajectory reconstruction with uncertainty estimation using mosaic registration

This paper addresses the problem of estimating the 3D trajectory and associated uncertainty of an underwater autonomous vehicle from a set of images of the seabed taken by an onboard camera. The presented algorithms resort to the use of video mosaics and build upon previous work on image registration and visual pose estimation. The pose estimation is accomplished in two steps. Firstly, a video mosaic is created automatically, covering a region of interest of the seabed. Then, after associating a 3D referential for the mosaic, the estimation of the camera position from a new view of the scene becomes possible.

The main contribution of this paper lies on the assessment of the performance of the 3D pose algorithms. In order to do this, an image sequence with available ground-truth is used for precise error measuring. A first-order error propagation analysis is presented, relating the uncertainty in the location of the match points with the uncertainty in the pose parameters. The importance of predicting the estimate uncertainty is emphasized by the fact that it can be used for comparing algorithms and for the on-line monitoring of the vehicle trajectory reconstruction quality.

Several iterative and non-iterative pose estimation methods are discussed, differing both on the criteria being minimized and on the required information about the camera intrinsic parameters. This information ranges from the full knowledge of the parameters, to the case where they are estimated using self-calibration from an image sequence under pure rotation. The implemented pose algorithms are compared for the accuracy and estimate covariance.     

Consensusability of multi-agent systems via observer with limited communication data rate

This paper investigates the consensusability of multi-agent systems via observer with limited communication data. A novel algorithm to determine the parameters of quantizer and encoder is provided. The observer-based consensusability with unlimited bandwidth and observer-based consensusability with communication data rate are discussed separately. Finally, a simulation is given to illustrate the results.     

Automatic urban modeling using volumetric reconstruction with surface graph cuts

The demand for 3D city-scale models has been significantly increased due to the proliferation of urban planning, city navigation, and virtual reality applications. We present an approach to automatically reconstruct buildings densely spanning a large urban area. Our method takes as input calibrated aerial images and available GIS meta-data. Our computational pipeline computes a per-building 2.5D volumetric reconstruction by exploiting photo-consistency where it is highly sampled amongst the aerial images. Our building surface graph cut method overcomes errors of occlusion, geometry, and calibration in order to stitch together aerial images and yield a visually coherent texture-mapped result. Our comparisons show similar quality to the manually modeled buildings of Google Earth, and show improvements over naive texture mapping and over space-carving methods. We have tested our algorithms with a 12 sq km area of Boston, MA (USA), using 4667 images (i.e., 280 GB of raw image data) and producing 1785 buildings.     

On climate reconstruction using bivalves: three methods to interpret the chemical signature of a shell

To improve our understanding of the climate process and to assess the human impact on current global warming, past climate reconstruction is essential. The chemical composition of a bivalve shell is strongly coupled to environmental variations and therefore ancient shells are potential climate archives. The nonlinear nature of the relation between environmental condition (e.g. the seawater temperature) and proxy composition makes it hard to predict the former from the latter, however. In this paper we compare the ability of three nonlinear system identification methods to reconstruct the ambient temperature from the chemical composition of a shell. The comparison shows that nonlinear multi-proxy approaches are potentially useful tools for climate reconstructions and that manifold based methods result in smoother and more precise temperature reconstruction.     

结合数据变换和启发式方法的模糊系统辨识

取代应用原始数据样本构造模糊模型的传统方法,提出应用数据变换技术和启发式方法简化模糊建模过程.对于变换后的数据,首先通过启发式方法确定模糊If-Then规则结论部分非模糊单值(即实数)的初始值,然后通过梯度下降学习方法进行精调.该方法不仅模型精度较高,而且收敛速度快.仿真实例验证了所提出的模型优于传统的方法.     

A methodology and a software tool for sensor data validation/reconstruction: Application to the Catalonia regional water network

In this paper, a sensor data validation/reconstruction methodology applicable to water networks and its implementation by means of a software tool are presented. The aim is to guarantee that the sensor data are reliable and complete in case that sensor faults occur. The availability of such dataset is of paramount importance in order to successfully use the sensor data for further tasks e.g. water billing, network efficiency assessment, leak localization and real-time operational control. The methodology presented here is based on a sequence of tests and on the combined use of spatial models (SM) and time series models (TSM) applied to the sensors used for real-time monitoring and control of the water network. Spatial models take advantage of the physical relations between different system variables (e.g. flow and level sensors in hydraulic systems) while time series models take advantage of the temporal redundancy of the measured variables (here by means of a Holt–Winters (HW) time series model). First, the data validation approach, based on several tests of different complexity, is described to detect potential invalid or missing data. Then, the reconstruction process is based on a set of spatial and time series models used to reconstruct the missing/invalid data with the model estimation providing the best fit. A software tool implementing the proposed data validation and reconstruction methodology is also described. Finally, results obtained applying the proposed methodology to a real case study based on the Catalonia regional water network is used to illustrate its performance.     

Selecting informative genes from microarray data by using hybrid methods for cancer classification

Gene expression technology, namely microarrays, offers the ability to measure the expression levels of thousands of genes simultaneously in biological organisms. Microarray data are expected to be of significant help in the development of an efficient cancer diagnosis and classification platform. A major problem in these data is that the number of genes greatly exceeds the number of tissue samples. These data also have noisy genes. It has been shown in literature reviews that selecting a small subset of informative genes can lead to improved classification accuracy. Therefore, this paper aims to select a small subset of informative genes that are most relevant for cancer classification. To achieve this aim, an approach using two hybrid methods has been proposed. This approach is assessed and evaluated on two well-known microarray data sets, showing competitive results. This work was presented in part at the 13th International Symposium on Artificial Life and Robotics, Oita, Japan, January 31–February 2, 2008     

Automated as-built 3D reconstruction of civil infrastructure using computer vision: Achievements,opportunities, and challenges

Image-based 3D reconstruction of civil infrastructure is an emerging topic that is gaining significant interest both in the scientific and commercial sectors of the construction industry. Reliable computer vision-based algorithms have become available over the last decade and they can now be applied to solve real-life problems in uncontrolled environments. While a large number of such algorithms have been developed by the computer vision and photogrammetry communities, relatively little work has been done to study their performance in the context of infrastructure. This paper aims to analyze the state-of-the-art in image-based 3D reconstruction and categorize existing algorithms according to different metrics that are important for the given purpose. An ideal solution is portrayed to show what the ultimate goal is. This will be followed by identifying gaps in knowledge and highlighting future research topics that could contribute to the widespread adoption of this technology in the construction industry. Finally, a list of practical constraints that make the 3D reconstruction of infrastructure a challenging task is presented.     

A comparative study on optimization methods for experiments with ordered categorical data

When conducting experiments, the selected quality characteristic should as far as possible be a continuous variable and be easy to measure. Due to the inherent nature of the quality characteristic or the convenience of the measurement technique and cost-effectiveness, the data observed in many experiments are ordered categorical. To analyze ordered categorical data for optimizing factor settings, there are three widely accepted approaches: Taguchi’s accumulation analysis, Nair’s scoring scheme and Jeng’s weighted probability scoring scheme. In this paper, a simpler method named the weighted SN ratio method for analyzing ordered categorical data is introduced. A case study involving optimizing the polysilicon deposition process for minimizing surface defects and achieving the target thickness in a very large-scale integrated circuit can demonstrate the four approaches. Finally, comparative analyses of efficiency for employing the four approaches to optimize factor settings are presented according to simulated experimental data that are normally, Weibull and Gamma distributed. From the results, it is obvious that the weighted SN ratio method has the properties of easy computation and uses one-step optimization to obtain the optimal factor settings. Its efficiency is slightly less than that of the scoring scheme, better than that of the accumulation analysis and the weighted probability-scoring scheme.     

Fast implementation of partial Gram-Schmidt algorithm using tree data structure with sequential row pointers data structure to solve structural problems (PGS)

In this paper, tree data structure with sequential row pointers data structure is developed and used to implement the Partial Gram-Schmidt triangularization algorithm. This algorithm is used to find the solution of linear equations arising from many structural and network problems where the original matrix is non-symmetric and highly sparse, that is, the ratio between zeros and nonzeros elements is very large. An algorithm is developed using the above data structure and results are compared with the method given in M.A. Ajiz, Incomplete Methods For Computer Structural Analysis, PhD Thesis, Queen's University Belfast, 1982 and Ajiz & Jennings, Int. J. Num. Meth. Engng., 20, 949–966, 1984. Conjugate gradients algorithm is used in conjunction with Partial Gram-Schmidt algorithm to obtain the solution of problems obtaining four figure accuracy.     

Analysing the power of deep learning techniques over the traditional methods using medicare utilisation and provider data

Deep Learning Technique (DLT) is the sub-branch of Machine Learning (ML) which assists to learn the data in multiple levels of representation and abstraction and shows impressive performance on many Artificial Intelligence (AI) tasks. This paper presents a new method to analyse the healthcare data using DLT algorithms and associated mathematical formulations. In this study, we have first developed a DLT to programme two types of deep learning neural networks, namely: (a) a two-hidden layer network, and (b) a three-hidden layer network. The data was analysed for predictability in both of these networks. Additionally, a comparison was also made with simple and multiple Linear Regression (LR). The demonstration of successful application of this method is carried out using the dataset that was constructed based on 2014 Medicare Provider Utilization and Payment Data. The results indicate a stronger case to use DLTs compared to traditional techniques like LR. Furthermore, it was identified that adding more hidden layers to neural network constructed for performing deep learning analysis did not have much impact on predictability for the dataset considered in this study. Therefore, the experimentation described in this article sets up a case for using DLTs over the traditional predictive analytics. The investigators assume that the algorithms described for deep learning is repeatable and can be applied for other types of predictive analysis on healthcare data. The observed results indicate, the accuracy obtained by DLT was 40% more accurate than the traditional multivariate LR analysis.     

Synergistic approach for mapping debris-covered glaciers using optical-thermal remote sensing data with inputs from geomorphometric parameters

Debris cover on glacier boundaries critically impedes the global inventorying of glaciers and confounds most of the techniques developed for semi-automated mapping of glaciers. Debris on the glacier (referred as supraglacial debris) and that occurring outside the glacier boundaries (referred as periglacial debris) being derived from a common source, i.e. the valley rock, tend to have a similar spectral response in the reflection region which renders them mutually indistinguishable. However, there exist temperature differences between them. This aspect has been considered in this remote sensing based study to distinguish between the supraglacial and periglacial debris in a test area in the Chenab basin, Himalayas, by inclusion of thermal infrared (TIR) bands in remote sensing data processing. A synergistic multisensor approach for the delineation of debris-covered glacier boundaries is used here which integrates the inputs from thermal (TERRA-ASTER sensor) and optical (IRS-P6-AWiFS sensor) remote sensing data, multispectral classification techniques and the DEM derived geomorphometric parameters. The results of this study corroborate earlier findings on utilization of temperature differences as one of the parameters in glacial studies. The proposed synergistic approach therefore appears useful in accurate mapping of debris-covered glaciers in the Himalayan region.