面向NURBS曲线构建的切片技术研究

激光点云数据NURBS曲线曲面重构一般采用参数化方法时,要求数据点是按照特殊规则排列的,即链式曲线和曲面网格.如果被测点是不规则空间排列,运用参数化方法会非常困难,拟合曲面可能会扭曲的很厉害.针对该问题,本文利用切片技术对散乱点进行曲线重建,首先采用曲率分析方法确定切片方向,然后以一定厚度的一系列平行平面与点云求交,对"相交点集"排序,再由参数化方法进行NURBS曲线重构.经实验验证,该方法在NURBS曲线重构中具有高效率性和计算稳定性.     

Massive-scale visual information retrieval towards city residential environment surveillance

Urban residential environment surveillance plays an important role in modern intelligent city. Satellite images have been applied in various fields, and the analysis and processing of satellite images has become an important means to obtain the information perceived by satellites. This paper focuses on city residential environment surveillance based on massive-scale visual information retrieval. Since the shortcomings of low contrast, blurred boundary, large amount of information and susceptibility to noise, the performance of satellite image segmentation is not satisfactory, which will affect residential environment surveillance. We design an improved rough set fuzzy C-means clustering algorithm combined with ant colony algorithm. More specifically, satellite images are classified based on the gradient of pixels according to the indistinguishable relation of the image combined with rough set theory. Then, the traditional fuzzy set-based fuzzy C-means clustering algorithm is applied to the satellite image segmentation technology. Subsequently, the improved algorithm-quantum ant colony algorithm and rough set fuzzy clustering C-means algorithm are combined to achieve accurate segmentation of satellite images. Afterwards, we propose a satellite image retrieval algorithm, which can assist city residential environment surveillance. Comprehensive experiment show that our proposed method is effective and robust in residential environment surveillance.     

Gray-level image denoising with an improved weighted sparse coding

The nonlocal self-similarity of images means that groups of similar patches have low-dimensional property. The property has been previously used for image denoising, with particularly notable success via sparse coding. However, only a few studies have focused on the varying statistics of noise in different similar patches during the iterative denoising process. This has motivated us to introduce an improved weighted sparse coding for gray-level image denoising in this paper. On the basis of traditional sparse coding, we introduce a weight matrix to account for the noise variation characteristics of different similar patches, while introduce another weight matrix to make full use of the sparsity priors of natural images. The Maximum A-Posterior estimation (MAP) is used to obtain the closed-form solution of the proposed method. Experimental results demonstrate the competitiveness of the proposed method compared with that of state-of-the-art methods in both the objective and perceptual quality.     

IaaS云安全研究综述

目前,在新一代大规模互联网迅猛发展的背景下,产生的数据量也随之持续增长,这就导致用户的本地设备难以满足海量数据的存储和计算需求。与此同时,云计算作为一种经济高效且灵活的模式,具有易于使用、随用随付、不受时间和空间限制的优势,彻底改变了传统IT基础设施的提供和支付方式,可以有效解决无限增长的海量信息存储和计算问题。因此,在没有昂贵的存储成本和计算资源消耗的情况下,资源有限的用户可以采用云服务提供商（Cloud Service Provider,CSP）为用户提供所期望的服务。其中,基础设施即服务（Infrastructure as a Service,IaaS）作为云计算的三种服务类型之一,将虚拟化、分布式计算和网络存储等技术结合,可以在互联网上提供和租用计算基础设施资源服务（如计算、存储和网络）。故云计算依靠IaaS层提供的计算基础设施资源,使用户不再需要购买额外设备,从而大大降低使用成本,同时也为上层服务奠定基础。然而,随着云计算服务的不断发展,基于IaaS的安全问题引起人们的关注。为了系统了解IaaS的安全研究进展和现状,本文对IaaS的安全问题以及学术界和工业界的解决方案进行了详细调查。首先,本文介绍IaaS的相关理论基础并对分析不同类型的云安全威胁。然后,从学术界现有研究出发,分析IaaS提供的计算、存储和网络服务中存在的安全威胁,并调查现有的解决方案。此外,对工业界中云服务提供商的IaaS安全服务进行重点调查,包括数据安全、网络防护和其他安全服务等方面。最终,展望未来IaaS云安全在学术和工业环境中的发展趋势。     

Transferable two-stream convolutional neural network for human action recognition

Human-Robot Collaboration (HRC), which enables a workspace where human and robot can dynamically and safely collaborate for improved operational efficiency, has been identified as a key element in smart manufacturing. Human action recognition plays a key role in the realization of HRC, as it helps identify current human action and provides the basis for future action prediction and robot planning. While Deep Learning (DL) has demonstrated great potential in advancing human action recognition, effectively leveraging the temporal information of human motions to improve the accuracy and robustness of action recognition has remained as a challenge. Furthermore, it is often difficult to obtain a large volume of data for DL network training and optimization, due to operational constraints in a realistic manufacturing setting. This paper presents an integrated method to address these two challenges, based on the optical flow and convolutional neural network (CNN)-based transfer learning. Specifically, optical flow images, which encode the temporal information of human motion, are extracted and serve as the input to a two-stream CNN structure for simultaneous parsing of spatial-temporal information of human motion. Subsequently, transfer learning is investigated to transfer the feature extraction capability of a pretrained CNN to manufacturing scenarios. Evaluation using engine block assembly confirmed the effectiveness of the developed method.     

利用张量投票的机载LiDAR点云道路骨架线提取

针对机载LiDAR数据中道路骨架线检测存在的噪声干扰问题,结合道路多层特征显著性,提出了一种基于道路最大宽度快速确定最小尺度因子的张量投票道路骨架线提取方法。将预处理后三维道路点云转化成二维强度图像,最小尺度因子参与图像球张量投票,利用极性特征分割道路边缘点;为了进一步增强道路线状特征,利用新的最小尺度因子再次进行球张量投票和棒张量投票,填补道路空洞,顺滑道路边界;细化处理获取道路骨架线。与数学形态学方法相比,该方法在噪声背景的道路数据中提取的道路线精度更高。     

Permutation-test-based clustering method for detection of dynamic patterns in Spatio-temporal datasets

Massive spatio-temporal data have been collected from the earth observation systems for monitoring the changes of natural resources and environment. To find the interesting dynamic patterns embedded in spatio-temporal data, there is an urgent need for detecting spatio-temporal clusters formed by objects with similar attribute values occurring together across space and time. Among different clustering methods, the density-based methods are widely used to detect such spatio-temporal clusters because they are effective for finding arbitrarily shaped clusters and rely on less priori knowledge (e.g. the cluster number). However, a series of user-specified parameters is required to identify high-density objects and to determine cluster significance. In practice, it is difficult for users to determine the optimal clustering parameters; therefore, existing density-based clustering methods typically exhibit unstable performance. To overcome these limitations, a novel density-based spatio-temporal clustering method based on permutation tests is developed in this paper. High-density objects and cluster significance are determined based on statistical information on the dataset. First, the density of each object is defined based on the local variance and a fast permutation test is conducted to identify high-density objects. Then, a proposed two-stage grouping strategy is implemented to group high-density objects and their neighbors; hence, spatio-temporal clusters are formed by minimizing the inhomogeneity increase. Finally, another newly developed permutation test is conducted to evaluate the cluster significance based on the cluster member permutation. Experiments on both simulated and meteorological datasets show that the proposed method exhibits superior performance to two state-of-the-art clustering methods, i.e., ST-DBSCAN and ST-OPTICS. The proposed method can not only identify inherent cluster patterns in spatio-temporal datasets, but also greatly alleviates the difficulty in selecting appropriate clustering parameters.     

存算分离的原位传感器观测接入方法

原位传感器是智慧城市建设的重要数据来源,其在城市资源协调、灾害预警、动态监测分析等领域发挥决定性作用.当前传感器观测接入方法未考虑传感器数据的流式特征,无统一的接入模型,导致无法统一过滤特定时空场景下的观测结果,传感器接入组件可复用性差.本文提出一种存算分离的原位传感器观测接入方法,以站点为中心的传感器统一接入模型为基础,将流式处理框架下的原位传感器观测接入过程分为数据获取、观测过滤与观测存储三个部分.实验证明该方法能够基于传感器统一接入模型有效接入网络结构异构的原位传感器站点,并实现对多个原位传感器观测结果特定时间、空间场景下的属性过滤.     

基于时刻表的多模式交通网络路径规划

城市交通系统正逐渐由原来的单一模式转变为相互连通的多模式,为了更准确地表达多模式交通网络系统,并满足个人出行时路线规划和时间预测的要求,该文以Oracle空间网络数据模型为建模基础,以武汉市为例,选取了道路网、公交路网和地铁路网三种路网模式构建了多模式交通路网,并且加入公交线路和地铁线路的时刻表因素,旨在使出行者通过该模型可以找到到达目的地的最快路线并预估该路线所需时间.     

HJ-1B和Landsat卫星蓝藻水华监测能力评估——以洱海为例

蓝藻水华是富营养化湖泊共同面临的问题。遥感技术为快速、大范围水华监测提供了可能,选取遥感数据应首先明确不同卫星的水华监测能力。以洱海为例,对比分析HJ-1B和Landsat卫星在内陆中小湖泊水华监测的时间和空间监测能力,评价两者在水华监测中的适用性及优势。结果表明:两者均能有效识别水华,提取水华分布细节信息,相比MODIS更适合用于中小湖泊水华监测;进一步分析表明,综合两者数据监测蓝藻水华,可以更加客观统计水华时间特征,描述水华空间分布发展规律,对于其它中小湖泊利用遥感手段辅助水华监测具有参考意义。