A new calibration model of camera lens distortion

Lens distortion is one of the main factors affecting camera calibration. In this paper, a new model of camera lens distortion is presented, according to which lens distortion is governed by the coefficients of radial distortion and a transform from ideal image plane to real sensor array plane. The transform is determined by two angular parameters describing the pose of the real sensor array plane with respect to the ideal image plane and two linear parameters locating the real sensor array with respect to the optical axis. Experiments show that the new model has about the same correcting effect upon lens distortion as the conventional model including all the radial distortion, decentering distortion and prism distortion. Compared with the conventional model, the new model has fewer parameters to be calibrated and more explicit physical meaning.     

Adaptive learning of dynamic Bayesian networks with changing structures by detecting geometric structures of time series

A dynamic Bayesian network (DBN) is one of popular approaches for relational knowledge discovery such as modeling relations or dependencies, which change over time, between variables of a dynamic system. In this paper, we propose an adaptive learning method (autoDBN) to learn DBNs with changing structures from multivariate time series. In autoDBN, segmentation of time series is achieved first through detecting geometric structures transformed from time series, and then model regions are found from the segmentation by designed finding strategies; in each found model region, a DBN model is established by existing structure learning methods; finally, model revisiting is developed to refine model regions and improve DBN models. These techniques provide a special mechanism to find accurate model regions and discover a sequence of DBNs with changing structures, which are adaptive to changing relations between multivariate time series. Experimental results on simulated and real time series show that autoDBN is very effective in finding accurate/reasonable model regions and gives lower error rates, outperforming the switching linear dynamic system method and moving window method.

Visualizing transport structures of time-dependent flow fields

This article focuses on the transport characteristics of physical properties in fluids-in particular, visualizing the finite-time transport structure of property advection. Applied to a well-chosen set of property fields, the proposed approach yields structures giving insights into the underlying flow's dynamic processes.     

Consistency-driven data quality management of networked sensor systems

With more and more real deployments of wireless sensor network applications, we envision that their success is nonetheless determined by whether the sensor networks can provide a high quality stream of data over a long period. In this paper, we propose a consistency-driven data quality management framework called Orchis that integrates the quality of data into an energy efficient sensor system design. Orchis consists of four components, data consistency models, adaptive data sampling and process protocols, consistency-driven cross-layer protocols and flexible APIs to manage the data quality, to support the goals of high data quality and energy efficiency. We first formally define a consistency model, which not only includes temporal consistency and numerical consistency, but also considers the application-specific requirements of data and data dynamics in the sensing field. Next, we propose an adaptive lazy energy efficient data collection protocol, which adapts the data sampling rate to the data dynamics in the sensing field and keeps lazy when the data consistency is maintained. Finally, we conduct a comprehensive evaluation to the proposed protocol based on both a TOSSIM-based simulation and a real prototype implementation using MICA2 motes. The results from both simulation and prototype show that our protocol reduces the number of delivered messages, improves the quality of collected data, and in turn extends the lifetime of the whole network. Our analysis also implies that a tradeoff should be carefully set between data consistency requirements and energy saving based on the specific requirements of different applications.     

Scanning Depth of Route Panorama Based on Stationary Blur

This work achieves an efficient acquisition of scenes and their depths along long streets. A camera is mounted on a vehicle moving along a straight or a mildly curved path and a sampling line properly set in the camera frame scans the 1D images over scenes continuously to form a 2D route panorama. This paper proposes a method to estimate the depth from the camera path by analyzing a phenomenon called stationary blur in the route panorama. This temporal blur is a perspective effect in parallel projection yielded from the sampling slit with a physical width. We analyze the behavior of the stationary blur with respect to the scene depth, vehicle path, and camera properties. Based on that, we develop an adaptive filter to evaluate the degree of the blur for depth estimation, which avoids error-prone feature matching or tracking in capturing complex street scenes and facilitates real time sensing. The method also uses much less data than the structure from motion approach so that it can extend the sensing area significantly. The resulting route panorama with depth information is useful for urban visualization, monitoring, navigation, and modeling.     

基于博弈理论的动态入侵响应

随着计算机网络的飞速发展,人们对互联网技术的依赖程度越来越高,因此确保网络系统的安全性也变得越来越重要.近年来,由于网络攻击事件的增多,入侵检测和响应技术越来越成为目前的研究热点之一.不过与其他安全技术得到广泛应用并取得良好的效果相比,入侵检测与响应的发展是滞后的.这一方面是由于入侵检测自身检测技术的限制,另一方面是由于目前的报警响应研究未能处理好系统的收益及攻击者策略变化等问题.针对上述第2个问题,提出了一种基于博弈理论的动态入侵响应DIRBGT模型.该模型一方面对攻击者和系统双方收益计算得比较全面,因此系统在报警响应后的收益得到了保证;另一方面还将攻击者可能的策略变化纳入模型当中,与单从系统一方推理最优响应的不稳定性相比,其最优解是稳定、可靠的.实验结果表明,DIRBGT模型有效提高了报警响应的准确性和效果.     

支持HLA仿真和并行绘制的统一对象模型研究

随着计算机硬件、软件、网络以及社会需求的发展,分布式交互仿真应用对大规模复杂场景和大量仿真实体对象的管理能力和绘制能力提出了较高的要求.针对现有HLA仿真应用中大规模复杂场景的视景仿真实时性较差、仿真实体对象管理与绘制对象管理是分离的等问题,提出了统一对象模型,包含异质实体对象树、操作记录列表和统一访问接口,实现了仿真实体对象和绘制对象的高效组织和统一管理.该统一对象模型在HLA仿真平台和并行绘制平台之间建立了高效的数据交换桥梁,减轻了两者集成的开发工作量,并有效地支持了大规模复杂场景和大量仿真实体对象的实时绘制,有助于提高仿真的实时性.该统一对象模型对于HLA仿真平台和并行绘制平台具有通用性.最后给出了实验结果和分析.     

抛物问题Mortar有限元的瀑布型多重网格法

对抛物问题的全离散格式采用Mortar型有限元逼近,构造了相应的瀑布型多重网格法,证明了该方法的最优性.     

基于动态融合蚁群遗传算法的医学图像配准

将基于动态融合的蚁群遗传算法作为一种新的图像配准优化算法应用在多模医学图像配准中。该算法以互信息作为相似性测度,生成初始信息素分布,采用蚁群算法搜索最优变换参数,其中动态融合策略提高了混合算法的搜索效率。仿真实验结果表明,该算法有效地避免信息函数的局部极值,减少大量重复运算,提高了配准的效率,配准结果具有良好的稳定性。