一种新的基于直线特征的摄像机自标定方法

通过引入场景的几何约束,提出了一种新的基于直线的摄像机自标定方法。利用射影几何中平行直线投影的交点与光心的连线平行于该平行直线的性质,给出了摄像机内参数的线性求解方法。只假设场景中存在垂直的平行直线,相对于文献［1］而言,其适用的情况更为广泛。实验结果验证了方法的有效性。     

Pano Video:摄像机运动建模及从视频估计摄像机运动参数的一种方法

通过给摄像机平移、旋转与变焦等运动建模，并把运动模型与基于象素点亮度变化的方法相结合来估计摄像机运动参数。用得到的运动参数可以把视频构造成一幅全景图，全景图可广泛应用于视频压缩与检索。实验表明，该方法可成功地应用于视频会议系统中的视频压缩与视频检索。     

PanoVideo:摄像机运动建模及从视频估计摄像机运动参数的一种方法

通过给摄像机平移、旋转与变焦等运动建模，并把运动模型与基于象素点亮度变化的方法相结合来估计 摄像机运动参数。用得到的运动参数可以把视频构造成一幅全景图，全景图可广泛应用于视频压缩与检索。实验表 明，该方法可成功地应用于视频会议系统中的视频压缩与视频检索。     

一种新的摄像机线性标定方法

计算机视觉中,在对景物进行定量分析或对物体进行精确定位时,都需要进行摄像机标定,即准确确定摄影机的内部参数和外部参数,因此寻找新的快速有效的摄像机标定计算方法是计算机视觉应用中的一个重要问题。为也快速有效地进行摄像机的标定,并针对常用的带有一阶径向畸变的摄像机模型,提出了一种线性求解摄像机参数的标定方法,它可分步标定各参数,且全部采用线性方法求解,从而避免了非线性优化中的不稳定性,使得算法更为实用,简单快捷。实验结果表明,该方法具有较高的标定精度,是一种实用的标定方法。     

摄像机线性三步定标方法研究

计算机视觉中，在对景物进行定量分析或对物体进行精确定位时，都需要进行摄像机标定，即准确确定摄像机的内部参数和外部参数。由于真实的摄像机光学模型存在很多类型的畸变，因而导致透视投影关系是非线性的。为了解决标定过程的非线性最优化问题，针对常用的带有一阶径向畸变的摄像机模型，提出了一种新的线性三步摄像机定标方法，即首先通过径向排列约束计算摄像机参数的旋转矩阵、x轴平移向量和y轴平移向量；然后根据透视投影的交比不变性解算一阶径向畸变参数；最后利用求得的摄像机参数建立有效焦距和z轴平移向量的线性方程，采用最小二乘法来得到线性解。实验表明，该方法简单快捷，不仅具有较高的标定精度，而且解决了原有算法采用非线性搜索寻优可能存在的解不稳定的问题。     

一种求解摄像机内参数的非线性方法

提出了一种求解摄像机内参数的非线性方法。首先对空间单个矩形的图像获得含参数，的圆环点的图像，其中r为该矩形的长宽比；进而由圆环点对摄像机内参数的约束获得一组非线性方程；通过把非线性方程组求解问题转为含3个参数的最优化问题来确定摄像机的内参数。该方法的特点在于，无需知道矩形的任何信息，也容易将所获得的非线性方程组转化为最优化问题。实验结果表明，该方法具有较高的求解精度。     

A Constructive Graphical Model Approach for Knowledge-Based Systems: A Vehicle Monitoring Case Study

Graphical models have been widely applied to uncertain reasoning in knowledge-based systems. For many of the problems tackled, a single graphical model is constructed before individual cases are presented and the model is used to reason about each new case. In this work, we consider a class of problems whose solution requires inference over a very large number of models that are impractical to construct a priori. We conduct a case study in the domain of vehicle monitoring and then generalize the approach taken. We show that the previously held negative belief on the applicability of graphical models to such problems is unjustified. We propose a set of techniques based on domain decomposition, model separation, model approximation, model compilation, and re-analysis to meet the computational challenges imposed by the combinatorial explosion. Experimental results on vehicle monitoring demonstrated good performance at near-real-time.     

一种基于点对的相机几何标定方法

提出了一种基于点对的几何标定方法,用于标定相机外部参数．该方法考虑点对而不是单个点与相机的几何关系．首先根据三个标定点对计算旋转矩阵且不需要任何点对的精确三维信息．然后根据相机和一个点对的几何关系计算平移且只需这个点对的相对位置是已知的．整个标定过程不需要任何点对的绝对位置信息．试验结果表明了该方法的有效性．     

一种基于模型的单相机姿态估计方法

利用单幅影像测量目标在空间的位置和姿态参数(Pose)在目标定位、场景监测与更新、目标识别、自动导航等领域有着广泛的应用。目前有很多方法用于解决该问题,这些方法按照目标模型点与对应成像点的对应关系是否已知而分为两大类:一类是对应性已知条件下求解Pose;另一类是对应性未知条件下同时求解Pose与对应性问题。该文提出了一种新的采用单幅影像求解目标位置与姿态参数的方法。与已有的方法不同,该方法完全回避了与Pose问题并存的对应性问题。它利用目标模型投影与真实影像中目标区域的重合程度建立一个包含Pose参数的能量函数,将Pose求解转换为能量最优化问题,并采用了Hooke-Jeeves算法求解这个最优问题。多组仿真数据的仿真实验结果表明,该方法是正确和有效的。     

The Motor Extended Kalman Filter: A Geometric Approach for Rigid Motion Estimation

In this paper the motor algebra for linearizing the 3D Euclidean motion of lines is used as the oretical basis for the development of a novel extended Kalman filter called the motor extended Kalman filter (MEKF). Due to its nature the MEKF can be used as online approach as opposed to batch SVD methods. The MEKF does not encounter singularities when computing the Kalman gain and it can estimate simultaneously the translation and rotation transformations. Many algorithms in the literature compute the translation and rotation transformations separately. The experimental part demonstrates that the motor extended Kalman filter is an useful approach for estimation of dynamic motion problems. We compare the MEKF with an analytical method using simulated data. We present also an application using real images of a visual guided robot manipulator; the aim of this experiment is to demonstrate how we can use the online MEKF algorithm. After the system has been calibrated, the MEKF estimates accurately the relative position of the end-effector and a 3D reference line. We believe that future vision systems being reliably calibrated will certainly make great use of the MEKF algorithm.     

A Minimum Cost Approach for Segmenting Networks of Lines

The extraction and interpretation of networks of lines from images yields important organizational information of the network under consideration. In this paper, a one-parameter algorithm for the extraction of line networks from images is presented. The parameter indicates the extracted saliency level from a hierarchical graph. Input for the algorithm is the domain specific knowledge of interconnection points. Graph morphological tools are used to extract the minimum cost graph which best segments the network.We give an extensive error analysis for the general case of line extraction. Our method is shown to be robust against gaps in lines, and against spurious vertices at lines, which we consider as the most prominent source of error in line detection. The method indicates detection confidence, thereby supporting error proof interpretation of the network functionality. The method is demonstrated to be applicable on a broad variety of line networks, including dashed lines. Hence, the proposed method yields a major step towards general line tracking algorithms.     

新兴数字视频稳像中相机运动估计技术综述

为了解当前数字视频稳像中相机运动估计技术的发展现状、存在问题和发展前景,对其进行了研究。通过对相关文献的归纳和分析,将新兴数字视频稳像中相机运动估计技术分为3D运动估计、传感器辅助运动估计以及新兴2D运动估计,并分别讨论了各种运动估计技术的研究现状、研究成果、不足和挑战。最后,结合技术和市场因素对数字视频稳像中相机运动估计技术的发展前景进行了展望。研究表明,当前数字视频稳像中相机运动估计技术在实时应用和估计精度上都有待改善。     

A Linear Approach for Depth and Colour Camera Calibration Using Hybrid Parameters

Many recent applications of computer graphics and human computer interaction have adopted both colour cameras and depth cameras as input devices. Therefore, an effective calibration of both types of hardware taking different colour and depth inputs is required. Our approach removes the numerical difficulties of using non-linear optimization in previous methods which explicitly resolve camera intrinsics as well as the transformation between depth and colour cameras. A matrix of hybrid parameters is introduced to linearize our optimization. The hybrid parameters offer a transformation from a depth parametric space (depth camera image) to a colour parametric space (colour camera image) by combining the intrinsic parameters of depth camera and a rotation transformation from depth camera to colour camera. Both the rotation transformation and intrinsic parameters can be explicitly calculated from our hybrid parameters with the help of a standard QR factorisation. We test our algorithm with both synthesized data and real-world data where ground-truth depth information is captured by Microsoft Kinect. The experiments show that our approach can provide comparable accuracy of calibration with the state-of-the-art algorithms while taking much less computation time (1/50 of Herrera’s method and 1/10 of Raposo’s method) due to the advantage of using hybrid parameters.     

Critical Success Factors for Data Governance: A Theory Building Approach

The objective of this research study is to present the critical success factors (CSFs) for data governance (DG). This paper reports on a single case study where data are gathered through semi-structured interviews following the CSF approach and analyzed by applying open, axial, and selective coding techniques. The findings are presented as seven CSFs, which are ranked in order of importance. Furthermore, we highlight the need to better understand the relationships (interconnectedness) between the CSFs.     

摄像机线性模型标定中一种投影矩阵的修正方法

线性模型摄像机标定中,由于不考虑非线性畸变,使投影矩阵存在着标定误差,并影响三维重建的精度。在分析投影矩阵标定过程的基础上,提出了一种修正方法,它是将三维重建误差折算到投影矩阵中,通过解超定线性方程组求出参数的偏差值并构造新的投影矩阵。用修正后的矩阵进行三维重建,提高了重建的精度。     

A Polynomial Approach to the Constructive Induction of Structural Knowledge

The representation formalism as well as the representation language is of great importance for the success of machine learning. The representation formalism should be expressive, efficient, useful, and applicable. First-order logic needs to be restricted in order to be efficient for inductive and deductive reasoning. In the field of knowledge representation, term subsumption formalisms have been developed which are efficient and expressive. In this article, a learning algorithm, KLUSTER, is described that represents concept definitions in this formalism. KLUSTER enhances the representation language if this is necessary for the discrimination of concepts. Hence, KLUSTER is a constructive induction program. KLUSTER builds the most specific generalization and a most general discrimination in polynomial time. It embeds these concept learning problems into the overall task of learning a hierarchy of concepts.     

Detecting and Handling Unreliable Points for Camera Parameter Estimation

The popularly used DLT method sometimes fails to give reliable camera parameter estimation. It is therefore important to detect the unreliability and provide the corresponding solutions. Based on a complete framework of invariance for six points, we construct two evaluation functions to detect the unreliability. The two evaluation functions do not involve any computations for the camera projective matrix or optical center and thus are efficient to perform the detection. Then, the guidelines corresponding to the different detection results are presented. In particular, a filtering RANSAC method to remove the detected unreliable points is provided. The filtering RANSAC proves to be successful in removing the unreliable points even if these points are of a large proportion.     

A Coevolutionary Algorithm for Balancing and Sequencing in Mixed Model Assembly Lines

A mixed model assembly line is a production line where a variety of product models are produced. Line balancing and model sequencing problems are important for an efficient use of such lines. Although the two problems are tightly interrelated with each other, prior researches have considered them separately or sequentially. This paper presents a new method using a coevolutionary algorithm that can solve the two problems at the same time. In the algorithm, it is important to promote population diversity and search efficiency. We adopt a localized interaction within and between populations, and develop methods of selecting symbiotic partners and evaluating fitness. Efficient genetic representations and operator schemes are also provided. When designing the schemes, we take into account the features specific to the problems. Also presented are the experimental results that demonstrate the proposed algorithm is superior to existing approaches.     

基于故障树最小割集和最小路集的诊断方法研究

利用故障树的原理和方法,对基于故障树最小割集和最小路集的诊断方法进行了研究。首先给出了故障树最小割集和最小路集的数学描述,在此基础上开发了基于故障树最小割集和最小路集的诊断推理策略,最后提出了测点配置和优化的方法。     

Modeling Massive RFID Data Sets: A Gateway-Based Movement Graph Approach

Massive Radio Frequency Identification (RFID) data sets are expected to become commonplace in supply chain management systems. Warehousing and mining this data is an essential problem with great potential benefits for inventory management, object tracking, and product procurement processes. Since RFID tags can be used to identify each individual item, enormous amounts of location-tracking data are generated. With such data, object movements can be modeled by movement graphs, where nodes correspond to locations and edges record the history of item transitions between locations. In this study, we develop a movement graph model as a compact representation of RFID data sets. Since spatiotemporal as well as item information can be associated with the objects in such a model, the movement graph can be huge, complex, and multidimensional in nature. We show that such a graph can be better organized around gateway nodes, which serve as bridges connecting different regions of the movement graph. A graph-based object movement cube can be constructed by merging and collapsing nodes and edges according to an application-oriented topological structure. Moreover, we propose an efficient cubing algorithm that performs simultaneous aggregation of both spatiotemporal and item dimensions on a partitioned movement graph, guided by such a topological structure.