基于结构光和极线约束的三维重建

结构光可以简单、快速并且精确地对被测物体进行三维信息提取,从而得到三维数据点云.而在结构光三维信息获取系统中,条纹的准确检测是影响系统精度的关键因素之一.为了改善这一问题,先利用格雷码对图像条纹进行粗定位,再结合相位码准确地对图像务纹进行细定位并利用极线约束把图像匹配过程中的二维搜索降为一维搜索.结构光条纹和极线的交点就是图像的匹配点,对匹配点空间前方交会即可得到物体的三维点云.反复试验表明,该方法能取得较好的效果.     

结构光三维获取系统条纹中心线检测

结构光三维信息获取系统中,条纹中心的准确检测是影响系统精度的关键因素之一。传统的基于方向模板的方法带来几个问题:(1)专注于像素点的提取,忽略像素之间的结构光点(;2)重建的物体表面粗糙。为提高结构光条纹中心线的提取精度和提高重建对象的表面光滑度,提出了一种基于曲线拟合的结构光条纹中心的检测方法。这种方法基于Bezier曲线拟合,结合多帧平均和方向模板。实验表明采用这种方法可以获得比较高的提取精度和比较好的表面光滑度。     

大曲率激光条纹中心线提取方法研究

激光三维扫描中线激光条纹中心位置的确定直接决定了检测的精度。检测系统需要在恶劣工况下快速完成图像采集和处理,精度和实时性要求高。在铆钉表面激光条纹曲率变化大且实时性要求高的情况下,采用了基于并行形态学细化和方向模板的灰度重心法,对图像光条ROI进行运算,准确高速提取激光条纹中心线。该方法检测速度快、精度高,满足检测系统设计要求。     

一种结合梯度锐化和重心法的光条中心提取算法

在基于线结构光的视觉测量系统中,激光条纹中心位置的准确提取是影响系统精度的关键因素之一。本文分析了光条中心提取算法的研究现状,并比较了现有算法的优缺点,提出了一种结合梯度锐化和重心法的光条中心提取算法,该算法首先利用梯度锐化提取出光条的边缘,根据提取出的边缘得到光条的近似中心,然后在光条近似中心左右的小区域内利用重心法提取光条中心。实验结果表明,该算法能够准确地提取出光条中心,具有很强的抗噪声能力,精度为亚像素级。     

手持线激光扫描视频三维重建中的运动线提取算法

为解决目前三维扫描设备结构复杂、价格偏高、操作繁琐等问题,设计一种利用手持线激光运动扫描物体的视频三维重建方案,并针对视频中运动激光平面提取的难点问题,提出有效的解决方法。该方法基于每个像元邻近时刻视频帧中灰度信息的变化序列,内插出激光条纹边界过该像元的时刻,使用时间空间变换方法求解过每个像元的激光条纹所处的光平面,再利用结构光测量原理逐像素获取扫描物体表面的三维结构信息。使用该方法实现的视频激光扫描,可以方便地对多次扫描结果进行点云融合,有效地避免单次扫描造成的点云漏洞,且较传统结构光扫描过程中的线特征提取方法(曲线拟合法、Hessian矩阵方法等),具有获得的点云更加均匀规整、密度更高等特点,有利于后期点云数据的处理。     

激光焊接焊缝质量视觉检测系统

研究了LabVIEW在结构光视觉焊缝质量检测方面的应用,提出了一种快速高效的、用于自动化焊接系统图像处理的算法,并开发了激光焊缝质量监控系统.首先使用背景差分算法清除非均匀光照造成的影响,利用类间方差法求解最佳阈值,最大限度地保留了焊缝结构光条纹区域的有效信息;接着设计了自适应形态学收缩算法,使焊缝条纹区域在每列上收缩至一个像素点,从而得到一个没有分叉的骨架:最后利用骨架信息,结合数据拟合和特征参数提取,实现了焊缝的三维重建.试验结果证明,视觉检测系统以及焊缝特征的测量结果令人满意.     

嵌入式系统中的快速光条中心提取算法

在嵌入式系统中,其各种资源有限,为实现由ARM9处理器构成的三维形貌测量系统,光条纹中心提取算法的时空效率至关重要。激光器投射的光条在其法线方向上灰度值呈现高斯分布,针对该种情况,提出应用条纹法线方向结合灰度重心法求条纹中心的方法。对获取的图像进行中值滤波,用最大类间方差法对图像进行阈值分割;利用阈值法粗略提取光条的中心,利用微分中值定理近似求出光条纹法线方向,在法线方向上利用灰度重心法求光条中心。在实验中移植OPENCV2.0嵌入式视觉库,实验结果表明,该方法能实时精确地提取光条中心,且能达到亚像素级。     

基于改进调制结构光的三维形状准确测量方法

针对结构光三维形状测量中投射结构光的相互反射而降低形状测量精度的问题,提出一种改进结构光三维形状测量方法.该方法是利用了直接反射和相互反射之间存在的相位差异,以格雷码为基础设计出新的调制结构光;向被测对象投射一系列调制结构光的正-反投影,并利用摄像机获取与其相对应的结构光条纹图像.通过该条纹图像的亚像素定位方法确定结构光的边缘.实验结果表明,该方法能够准确地分离或消除了相互反射,将提高结构光三维形状测量精度.     

SMT封装电路板三维在线检测技术

为了检测回流焊接之后SMT( Surface Mount Technology)封装电路板是否存在缺陷,设计并搭建了基于线结构光传感器的SMT封装电路板三维在线检测系统,通过线结构光扫描测量,获取SMT封装电路板表面三维数据。采用双传感器测量技术,有效减少数据丢失;研究了双传感器统一标定技术,可同时实现两个传感器的参数标定和坐标系统一。提出了自适应光条中心提取算法,对反射或散射影响而形成的光条图像噪声具有很好的抑制效果,能够提取准确的光条中心。实验表明系统测量精度可达到0.02 mm。系统测量得到的三维数据,可以为在线检测SMT封装电路板缺陷提供可靠的三维信息。     

精确提取线结构光条纹中心方法

线结构光三维坐标测量中,光条纹中心的精确快速提取直接影响测量精度与效率。提出了基于光条纹图像方向与重心法相结合的方法,首先用阈值法获取条纹中心初值,然后,通过光条纹图像的灰度梯度,计算条纹法线方向,最后,在法线方向用重心法提取光条纹重心。实验结果表明,该方法能快速精确地提取光条纹中心,精度达到亚像素级。     

液压伺服关节自适应模糊神经网络控制补偿方法

三自由度液压伺服关节在实现位置跟踪时存在跟踪误差,原因在于液压伺服关节是一个具有饱和、结构死区和强耦合的动力学系统.为了解决这一问题,建立了该关节的动力学模型.通过比较几种控制方法在该关节位置跟踪问题上存在的不足,提出了一种自适应模糊神经网络控制补偿方法.该方法采用样本训练自学习,自适应调整变增益系数.该方法不但消除了饱和、结构死区和强耦合引起的位置跟踪误差,而且解决了控制向量在大范围内变化实现准确位置跟踪.最后,通过仿真试验验证了该动力学系统是稳定的,提出的方法是可行的.     

Maximum-entropy meshfree method for compressible and near-incompressible elasticity

Numerical integration errors and volumetric locking in the near-incompressible limit are two outstanding issues in Galerkin-based meshfree computations. In this paper, we present a modified Gaussian integration scheme on background cells for meshfree methods that alleviates errors in numerical integration and ensures patch test satisfaction to machine precision. Secondly, a locking-free small-strain elasticity formulation for meshfree methods is proposed, which draws on developments in assumed strain methods and nodal integration techniques. In this study, maximum-entropy basis functions are used; however, the generality of our approach permits the use of any meshfree approximation. Various benchmark problems in two-dimensional compressible and near-incompressible small strain elasticity are presented to demonstrate the accuracy and optimal convergence in the energy norm of the maximum-entropy meshfree formulation.     

Kalman filter for adaptive learning of look-up tables with application to automotive battery resistance estimation

In online automotive applications, look-up tables are often used to model nonlinearities in component models that are to be valid over large operating ranges. If the component characteristics change with ageing or wear, these look-up tables must be updated online. Here, a method is presented where a Kalman filter is used to update the entire look-up table based on local estimation at the current operating conditions. The method is based on the idea that the parameter changes observed as a component ages are caused by physical phenomena having effect over a larger part of the operating range that may have been excited. This means that ageing patterns at different operating points are correlated, and these correlations are used to drive a random walk process that models the parameter changes. To demonstrate properties of the method, it is applied to estimate the ohmic resistance of a lithium–ion battery. In simulations the complete look-up table is successfully updated without problems of drift, even in parts of the operating range that are almost never excited. The method is also robust to uncertainties, both in the ageing model and in initial parameter estimates.     

Uncertainty modeling and spatial prediction by multi-Gaussian kriging: Accounting for an unknown mean value

In the analysis of spatial data, one is often interested in modeling conditional probability distributions, in order to assess the uncertainty in the values of the attribute under study and to predict functions of this attribute.This work examines three geostatistical models in which the attribute is assumed to be, up to a monotonic transformation, a realization of a Gaussian random field. In the first model, the mean of the Gaussian field is a known parameter and the conditional distributions at any set of locations are Gaussian, with expected values equal to simple kriging predictions and covariance matrix equal to that of the prediction errors. In the second model, the mean value is replaced by a random variable adding to the Gaussian field and whose prior variance is infinitely large, indicating a total lack of prior knowledge on the true mean. It is shown that the conditional distributions are still Gaussian, with expected values equal to ordinary kriging predictions and covariance matrix equal to that of the corresponding prediction errors. The third model considers a random drift that adds to the Gaussian field; the conditional distributions are then obtained by substituting universal kriging for simple or ordinary kriging.A computer program is provided to calculate recovery functions (tonnages, metal contents and mean values above given thresholds) and uncertainty measures (probability intervals and conditional variances) defined at point or block supports. The concepts are illustrated with a case study consisting of evaluating the recoverable resources in a porphyry copper deposit.     

A new approach to applying feedforward neural networks to the prediction of musculoskeletal disorder risk

A new and improved method to feedforward neural network (FNN) development for application to data classification problems, such as the prediction of levels of low-back disorder (LBD) risk associated with industrial jobs, is presented. Background on FNN development for data classification is provided along with discussions of previous research and neighborhood (local) solution search methods for hard combinatorial problems. An analytical study is presented which compared prediction accuracy of a FNN based on an error-back propagation (EBP) algorithm with the accuracy of a FNN developed by considering results of local solution search (simulated annealing) for classifying industrial jobs as posing low or high risk for LBDs. The comparison demonstrated superior performance of the FNN generated using the new method. The architecture of this FNN included fewer input (predictor) variables and hidden neurons than the FNN developed based on the EBP algorithm. Independent variable selection methods and the phenomenon of 'overfitting' in FNN (and statistical model) generation for data classification are discussed. The results are supportive of the use of the new approach to FNN development for applications to musculoskeletal disorders and risk forecasting in other domains.     

A comparison of usability evaluation methods for evaluating e-commerce websites

The importance of evaluating the usability of e-commerce websites is well recognised. User testing and heuristic evaluation methods are commonly used to evaluate the usability of such sites, but just how effective are these for identifying specific problems? This article describes an evaluation of these methods by comparing the number, severity and type of usability problems identified by each one. The cost of employing these methods is also considered. The findings highlight the number and severity level of 44 specific usability problem areas which were uniquely identified by either user testing or heuristic evaluation methods, common problems that were identified by both methods, and problems that were missed by each method. The results show that user testing uniquely identified major problems related to four specific areas and minor problems related to one area. Conversely, the heuristic evaluation uniquely identified minor problems in eight specific areas and major problems in three areas.     

一种基于模糊神经系统的图像去噪方法

提出一种对含有高斯噪声的数字图像的去噪方法,这种方法能够增强高斯噪声滤波器的性能,减少去噪对图像造成的模糊和失真。设计了一个模糊推理系统(FIS),并利用ANFIS训练这个FIS。通过训练可以调整、优化FIS的内部参数值。训练图像数据由计算机程序自动生成。优化后的FIS即可处理输入的图像数据,产生增强的图像。从结果图像的视觉效果和量化标准两方面的实验和分析,可以看出这种方法可基本消除高斯噪声滤波器产生的模糊和失真,提高滤波器性能。实验表明模糊神经系统可以应用于图像去噪问题。在合理地选择隶属度函数、规则和训练数据的前提下,会产生明显的图像增强效果。     

A semi-supervised probabilistic model for clustering large databases of complex images

Image content clustering is an effective way to organize large databases thereby making the content based image retrieval process much easier. However, clustering of images with varied background and foreground is quite challenging. In this paper, we propose a novel image content clustering paradigm suitable for clustering large and diverse image databases. In our approach images are represented in a continuous domain based on a probabilistic Gaussian Mixture Model (GMM) with the images modeled as mixture of Gaussian distributions in the selected feature space. The distance metric between the Gaussian distributions is defined in the sense of Kullback–Leibler (KL) divergence. The clustering is done using a semi-supervised learning framework where labeled data in the form of cluster templates is used to classify the unlabelled data. The clusters are formed around initially chosen seeds and are updated in the due course based on user inputs. In our clustering approach the user interaction is done in a structured way as to get maximum inputs from the user in a limited time. We propose two methods to carry out the structured user interaction using which the cluster templates are updated to improve the quality of the clusters formed. The proposed method is experimentally evaluated on benchmark datasets that are specifically chosen to include a wide variation of images around a common theme that is typically encountered in applications like photo-summarization and poses a major semantic gap challenge to conventional clustering approaches. The experimental results presented demonstrate the effectiveness of the proposed approach.     

Gaussian kernels for incomplete data

This paper discusses a method to estimate the expected value of the Gaussian kernel in the presence of incomplete data. We show how, under the general assumption of a missing-at-random mechanism, the expected value of the Gaussian kernel function has a simple closed-form solution. Such a solution depends only on the parameters of the Gamma distribution which is assumed to represent squared distances. Furthermore, we show how the parameters governing the Gamma distribution depend only on the non-central moments of the kernel arguments, via the second-order moments of their squared distance, and can be estimated by making use of any parametric density estimation model of the data distribution. We approximate the data distribution with the maximum likelihood estimate of a Gaussian mixture distribution. The validity of the method is empirically assessed, under a range of conditions, on synthetic and real problems and the results compared to existing methods. For comparison, we consider methods that indirectly estimate a Gaussian kernel function by either estimating squared distances or by imputing missing values and then computing distances. Based on the experimental results, the proposed method consistently proves itself an accurate technique that further extends the use of Gaussian kernels with incomplete data.     

Floating single sustain drive method for AC plasma display panel

This paper presents a method for driving an AC plasma display panel (PDP). This method separates the circuit ground for the sustain pulse generator from the system ground during the sustain period, so it is named the floating single sustain method. The problems observed in previous single sustain methods are solved by separating the ground systems. A drive waveform and circuit for the floating single sustain method are proposed, and a cost-effective method of connecting the drive circuit to the electrodes of a PDP is presented. Experimental results on a 42-in. WVGA single-scan PDP show that the proposed waveform and circuit are well suited for driving the PDP; all problems observed in the previous single sustain method are solved and the power consumption due to the sustain power leakage to the data drive circuit is eliminated.