Extracting area objects from raster image data

An algorithm for extracting the spatial objects contained in raster image data is presented. Input to the algorithm is a Siemens computer-aided design (SICAD)-Hygris raster file representing a classification of the plane. Output of the algorithm is a SICAD geographic information system (GIS) external file containing the topologically complete definitions of all area objects and their constituents, such as arcs and nodes, as implied by the classification. The algorithm is based on a one-pass plane sweep that processes the input raster data in a strictly sequential order. Processing takes place only if certain color changes, also called events, occur. Each event triggers a well-defined sequence of simple actions     

Road network extraction from high resolution satellite images

In this paper, an approach of roads network extraction from high resolution satellite images is presented. First, the approach extracts road surface from satellite image using one-class support vector machine(SVM). Second, the road topology is built from the road surface. The last output of the approach is a series of road segments which is represented by a sequence of points as well as the topological relations among them. The approach includes four steps. In the first step one-class support vector machine is used for classifying pixel of the satellite images to road class or non-road class. In the second step filling holes and connecting gaps for the SVM's classification result is applied through mathematical morphology close operation. In the third step the road segment is extracted by a series of operations which include skeletonization, thin, branch pruning and road segmentation. In the last step a geometrical adjustment process is applied through analyzing the road segment curvature. The experiment results demonstrate its robustness and viability on extracting road network from high resolution satellite images.     

道路网中基于RRN-Tree的CKNN查询

现有针对基于道路网络的CKNN查询研究,主要是将道路网络以路段和节点的形式进行建模,转化成基于内存的有向/无向图,该模型存在2个问题:一个是道路网络中路段数据量大,导致索引结构分支过多、移动对象更新频繁;另一个是图表示方法不能很好地处理十字路口转向、U型转弯等交通规则。针对此问题,提出道路网中基于RRN-Tree的移动对象CKNN查询算法,包括索引结构设计和移动对象查询算法设计,采用路线对道路网建模,基于网络边扩展方式,实现复杂条件下的道路网络CKNN查询。实验结果表明,在各种网络密度和兴趣点对象分布密度下,与经典的IMA/GMA算法相比,基于RRN-Tree索引方法的查询性能提高1.5倍~2.13倍。     

用Voronoi多边形扩展的空间数据模型

Voronoi图是一种基本几何结构 ,也是解决相关几何问题的有效的工具 .为了有效地解决 GIS中的空间目标间关系的动态构建、显示等问题 ,首先重点探讨了用 Voronoi图扩展的空间数据模型 ,然后主要从空间数据建模语义的角度出发 ,在研究 GIS面向对象的空间数据模型的基础上 ,提出了用 Voronoi多边形来部分地代替面向对象的数据模型间的关系定义 ,并给出了一个基于 Voronoi多边形的面向对象数据模型的框架 .该模型由于利用了Voronoi图具有的能良好地表示空间目标邻近关系的特性 ,并且由于通过空间目标的位置 ,能动态地获取和显示空间目标间的邻接关系 ,因而可以有效地弥补目前拓扑数据模型中 ,计算更新复杂及不能表示空间上邻近 ,而几何上不相接目标间的空间关系和栅格数据模型中不能有效地表达目标间拓扑关系的不足的问题 ,实践证明 ,这是一种较为理想的表示复杂空间关系的数据模型 .     

一种新的道路网络连续查询处理方法

基于道路网络的连续k近邻查询是移动对象数据库领域的研究重点和热点.提出了一种新的道路网络有向图模型,通过引入有向网络空间度量,利用基于内存的格网索引和线性链表结构来对移动对象当前位置和道路网络有向图模型进行存储和管理;基于有向距离度量提出了单向网络扩展(DNE)算法,以减少连续k近邻查询的网络扩展搜索代价.实验结果表明,DNE算法性能优于现有的连续k近邻查询处理算法.     

融合链接预测相似度矩阵的属性网络嵌入算法

在属性网络中,与节点相关联的属性信息有助于提升网络嵌入各种任务的性能,但网络是一种图状结构,节点不仅包含属性信息还隐含着丰富的结构信息。为了充分融合结构信息,首先通过定义节点的影响力特性、空间关系特征;然后根据链接预测领域基于相似度的定义构建相似度矩阵,将节点二元组中的关联向量映射到相似度矩阵这一关系空间中,从而保留与节点相关的结构向量信息;再基于图的拉普拉斯矩阵融合属性信息和标签特征,将上述三类信息集成到一个最优化框架中;最后,通过二阶导数求局部最大值计算投影矩阵获取节点的特征表示进行网络嵌入。实验结果表明,提出的算法能够充分利用节点二元组的邻接结构信息,相比于其他基准网络嵌入算法,本模型在节点分类任务上取得了更好的结果。     

Knowledge Discovery in Complex Objects

Learning concepts and rules from structured (complex) objects is a quite challenging but very relevant problem in the area of machine learning and knowledge discovery. In order to take into account and exploit the semantic relationships that hold between atomic components of structured objects, we propose a knowledge discovery process, which starts from a set of complex objects to produce a set of related atomic objects (called contexts). The second step of the process makes use of the concatenation product to get a global context in which binary relations of individual contexts coexist with relations produced by the application of some operators to individual contexts. The last step permits the discovery of concepts and implication rules using the concept lattice as a framework in order to discover and interpret nontrivial concepts and rules that may relate different components of complex objects. This paper focuses on two main steps of the knowledge discovery process, namely data mining and interpretation.     

SkelTre

Terrestrial laser scanners capture 3D geometry of real world objects as a point cloud. This paper reports on a new algorithm developed for the skeletonization of a laser scanner point cloud. The skeletonization algorithm proposed in this paper consists of three steps: (i) extraction of a graph from an octree organization, (ii) reduction of the graph to a skeleton, and (iii) embedding of the skeleton into the point cloud. For these three steps, only one input parameter is required. The results are validated on laser scanner point clouds representing 2 classes of objects; first on botanic trees as a special application and secondly on popular arbitrary objects. The presented skeleton found its first application in obtaining botanic tree parameters like length and diameter of branches and is presented here in a new, generalized version. Its definition as Reeb Graph, proofs the usefulness of the skeleton for applications like shape analysis. In this paper we show that the resulting skeleton contains the Reeb Graph and investigate the practically relevant parameters: centeredness and topological correctness. The robustness of this skeletonization method against undersampling, varying point density and systematic errors of the point cloud is demonstrated on real data examples.     

从航空遥感图像中自动提取主要道路

如果能自动地从航空遥感图像中提取出道路网,将会简化城市地物目标的分类和测量过程.该文根据城市主要道路在图像中的特性进行了模型化处理,进而提出了基于直线的、自动提取的方法.算法重点考虑到成像过程中必然引入的各种噪声,用高斯分布函数模糊化直线参数,使提取具有良好的稳健性.算法中的参数选择都是通过理论分析所得,因此,提取过程实现了自动化.实验结果表明,该方法能够从实际航空遥感图像中提取出主要道路网.     

Topology of surfaces modelling bridges and tunnels in 3D-GIS

Bridges and tunnels are semantically meaningful objects in Geographical Information Systems (GIS), playing an important role in terms of reachability between locations. From a topological point of view, bridges and tunnels on surfaces are captured by the notion of a handle. In this paper, we extend an existing axiomatic characterization of 3D surfaces, which guarantees consistency between geometry and topology, to the case of handles. Furthermore, we present a method which verifies the occurrence of handles in surfaces, thereby contributing to the semantic-topological consistency in GIS. We close the gap between the global topological definition of handles in surfaces and the local definition of semantical handle objects in GIS - tunnels, bridges, arcades. Our method bases on a classification of cycles in two types. We give an efficient algorithm for discriminating both using standard methods from graph theory. This classification in addition contributes to the correctness of editing operations in GIS.     

基于GIS路网的公交路线轨迹算法

为解决公交路线轨迹偏移路网以及在GIS路网信息缺失尤其是乡村道路情况下的公交轨迹描绘.论文首先通过深入分析公交车辆GPS数据,分别聚类出线路上下行轨迹点;其次,轨迹点清洗并排序;再次,结合GIS路网基础信息进行地图匹配;最后,根据改进的Dijkstra算法解决路网拓扑结构缺失情况下制作出公交路线轨迹.将该算法实施在A市35条公交线路上,线路匹配成功率为85%,未匹配成功线路由于样本缺失或者路网基础信息错误导致,可见该算法具有较好的准确率和实用性.     

Object-based road extraction from satellite images using ant colony optimization

Increasing demands for up-to-date road network and the availability of very-high-resolution (VHR) satellite images as well as the popularity of high-speed computers provide motivation and preliminary materials for researchers to propose more advanced approaches in order to increase the automation and robustness of road extraction strategies. In this article, road characteristics are modelled via object-based image analysis (OBIA). Object-based information is embedded as heuristic information in the ant colony optimization (ACO) algorithm for handling the road network extraction problem. A new neighbourhood definition in object space is introduced, which affects the transition rule in order to decrease the road gaps. Furthermore, an innovative desirability function for ACO is designed, which extracts the road objects, competently. The experimental results demonstrate the efficiency of the proposed algorithm for road extraction from VHR images. Moreover, the results of two state-of-the-art methods are compared with the proposed algorithm.     

A novel unsupervised 3D skeleton detection in RGB-D images for video surveillance

In this paper we present a novel moment-based skeleton detection for representing human objects in RGB-D videos with animated 3D skeletons. An object often consists of several parts, where each of them can be concisely represented with a skeleton. However, it remains as a challenge to detect the skeletons of individual objects in an image since it requires an effective part detector and a part merging algorithm to group parts into objects. In this paper, we present a novel fully unsupervised learning framework to detect the skeletons of human objects in a RGB-D video. The skeleton modeling algorithm uses a pipeline architecture which consists of a series of cascaded operations, i.e., symmetry patch detection, linear time search of symmetry patch pairs, part and symmetry detection, symmetry graph partitioning, and object segmentation. The properties of geometric moment-based functions for embedding symmetry features into centers of symmetry patches are also investigated in detail. As compared with the state-of-the-art deep learning approaches for skeleton detection, the proposed approach does not require tedious human labeling work on training images to locate the skeleton pixels and their associated scale information. Although our algorithm can detect parts and objects simultaneously, a pre-learned convolution neural network (CNN) can be used to locate the human object from each frame of the input video RGB-D video in order to achieve the goal of constructing real-time applications. This much reduces the complexity to detect the skeleton structure of individual human objects with our proposed method. Using the segmented human object skeleton model, a video surveillance application is constructed to verify the effectiveness of the approach. Experimental results show that the proposed method gives good performance in terms of detection and recognition using publicly available datasets.

     

基于骨架层次分解的目标的图表示

基于骨架的目标表示技术是模式识别和计算机视觉的重要研究内容,近年来人们提出了许多骨架化算法,但是有关利用骨架信息表示并识别目标的研究还非常有限。Ablameyko等1996年提出了通过分解由距离标号的骨架为有意义的结构基元从而获得目标的层次结构图的方法。该图可以准确地刻画基元之间的拓扑关系,但是它对于骨架中的噪声比较敏感。主要表现为噪声基元破坏其它基元的完整性和图的稳定性。该文采用将分支编组为分支链以及构造多尺度结构图的改进策略来克服这些缺点,最终获得了目标的节点数更小、节点显著度更高、节点间连接关系更稳定的多尺度图,从而显著地提高后续利用不精确图匹配技术进行目标识别的效率。这项技术已经被应用于一个基于形状特征的图像数据库检索系统中。     

分布式交通仿真路网模型的设计与持久化

针对传统仿真路网模型在大规模路网描述及数据共享、操作与维护上的不足,提出了1种分布式交通仿真路网模型的设计与持久化方案。首先提出了三层路网模型描述方案,导入并简化了地理信息系统（GIS）数据,再现了真实路网信息。然后在此基础上设计了基于关联映射的路网数据结构,实现了数据的分层存储,同时对数据进行对象关系映射（ORM）持久化,提高了数据的可操作性与易维护性。最后给出了面向对象路网数据操作的示例。     

Structural matching in computer vision using probabilisticrelaxation

In this paper, we develop the theory of probabilistic relaxation for matching features extracted from 2D images, derive as limiting cases the various heuristic formulae used by researchers in matching problems, and state the conditions under which they apply, We successfully apply our theory to the problem of matching and recognizing aerial road network images based on road network models and to the problem of edge matching in a stereo pair. For this purpose, each line network is represented by an attributed relational graph where each node is a straight line segment characterized by certain attributes and related with every other node via a set of binary relations     

基于数学形态学的道路遥感影像特征提取及网络分析

如何从遥感图象上提取道路特征已有多种方法，如边缘探测与追踪、线性滤波、利用各种空间关系进行道路特征识别，基于知识的道路网络提取以及数学形态学等，但尚有许多问题有待解决。为了方便GIS应用以及地图更新，提出了一种基于数学形态学的道路网络分析方法，用于对遥感图象上已分类的道路信息进行各种处理，以便得到所需的道路网络。该方法与步骤为首先将道路影像二值化，同时进行噪音去除、断线连接、细化，并通过将栅格数据转换成矢量形式来得到基本的道路网络；然后对基本道路网络进行分析、连接、选取；最后用Douglas-Peuker算法对道路进行平滑处理与表示来得到最终提取的道路网络，并以南京市江宁经济开发区SPOT、高分辨率IKONOS图象为例进行了实验。道路特征提取的结果与目视解译结果进行比较的结果表明，该道路提取方法对道路发展相对较快的区域更为有效，且提取精度较高。该方法对土地管理规划部门非常有价值，是进行GIS与地图道路更新的有效方法。     

含有平面结构场景的捆绑调整

捆绑调整是计算机视觉中三维结构恢复过程的重要步骤. 捆绑调整通常将空间中点与点坐标的调整视为相互独立的过程,但是在包含有自然物和人工物的场景中,由于存在多余的自由度,这种调整方法会导致优化结果偏离真值. 提出了一种带有共面约束和平面夹角约束的捆绑调整,用于优化带有平面的场景. 借助新的参数化方法,共面约束和夹角约束可以方便地进行表示,并且带有这两类约束的捆绑调整求解过程,仍然是一个无约束的非线性最小二乘问题. 实验结果表明,这种带有先验信息的捆绑调整提供了对结构的更准确估计. 由于先验信息的加入,增强型法方程的维度变高,借助了稀疏的求解技术和预条件子方法,大大降低了求解时间. 最后,为了在实际应用中能够自动寻找出夹角约束,提出了一种基于最大完全图的贪心方法,该方法尽可能多地保留了夹角约束.