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栅格地图矢量化关键技术研究与实现 总被引:9,自引:1,他引:9
在GIS中,采用扫描仪录入地图数据的难点为地图各要素的分割、细化和矢量化等问题。该文对相关技术进行了分析比较,并用数学形态学相关理论方法实现了对扫描图像中具有同一线型但不同线宽的线状要素的分类提取、细化和矢量化。 相似文献
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研究了自动矢量化地图所需的理论,并基于.NET平台进行了算法实现。以实现自动矢量化为目标,按照传统获取矢量化数据的流程进行探索。从数字图像处理和数学形态学的相应理论角度出发,分析了自动矢量化系统的功能;讨论了扫描电子地图二值化的实现、腐蚀膨胀和开闭运算理论在电子地图去噪中的应用、基于模式识别技术的图元细化与识别等难点技术。针对上述技术,在.NET平台上,基于GDI+技术,通过实例验证了自动矢量化系统的可行性及有效性。 相似文献
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该文分析了栅格图像矢量化的常用方法存在的问题,介绍并简单评价了多种主要的改进方法,方便技术人员根据具体需要快速的选择合适的矢量化方法,最后,就栅格图像矢量化的研究方向给出了一些建议。 相似文献
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基于彩色地图的交互式矢量化方法 总被引:3,自引:0,他引:3
本文介绍了直接基于彩色地图的交互式矢量化方法。这种方法通过样本学习、色彩距离度量、模糊选点等特点,较好地解决了如等高线一类的细线型要素的矢量化要求,达到了半自动的程度,具有较大的实际意义。 相似文献
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彩色扫描地图中线目标的矢量化方法 总被引:1,自引:0,他引:1
地图要素的矢量化是从扫描地图中获取地理信息的重要问题之一.通过分析彩色扫描地图的颜色特征,提出了一种线目标半自动矢量化的方法.该方法通过在用户选择的线目标上加一个滑动窗,采用颜色空间转换、K-均值聚类和区域生长相结合的方法对窗口内的当前线目标进行自适应分割;再通过不断地沿前进方向移动窗口和对窗口内的线目标进行分割、细化及序贯跟踪,来完成线目标的矢量化.实验结果表明,文中方法快速、准确、适应性强,对于低对比度、低信噪比的彩色扫描地图尤为有效. 相似文献
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针对占据栅格地图最佳栅格大小缺乏理论计算方法的问题,提出了以准确度与信息量为变量的代价函数来评估占据栅格地图的精度,从而获得最佳栅格大小的方法.首次提出了以"有义地图率"来表征占据栅格地图准确度的新概念,并通过数学推导给出了有义地图率的理论计算方法,揭示了栅格大小、传感器精度与有义地图率之间的关系,并以栅格数量来表征地图信息量.最后,通过仿真验证了有义地图率计算公式的正确性,同时通过RplidarA2雷达测扫实验验证了所提出的最佳栅格大小计算方法的正确性,进一步证实了有义地图率是地图的一种本征属性. 相似文献
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彩色栅格交通地图图像中道路识别与提取 总被引:4,自引:0,他引:4
在地图图像数学模型的基础上,根据地图中各类对象的颜色特征对地图图像进行规范化处理;在分析地图图像中噪声特征的基础上,利用噪声的自身特征和数学形态学的基本疗法来消除地图图像中的噪声,以达到识别与提取完整的道路网络的目的.该方法对道路欠识别进行了处理,并以实例来验证其对道路网络识别与提取的过程及其效果. 相似文献
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对于小型船务公司,为了节省成本,只想取得本公司业务内的航道电子地图。本文采用内河航道图的矢量化方法采集数据,生成内河航道电子地图。该方法采用区域平均滤波和航道曲线插值拟合等算法,按照"点、线、面"3个图层采集数据,实现了区域航道电子地图。试验结果表明,所开发的内河航道电子地图软件,操作方便,界面清楚,精度良好,运算效率高,并具有较好的可维护性,完全适用于小型船务公司的应用要求。 相似文献
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首先采用模板匹配、特征抽取等方法提取城市和道路的标识,这些标识对后面的道路的提取有着重要的作用;然后根据道路的等级,在颜色基础上利用道路的特征分层提取道路图层;最后对道路进行细化,依据城市与道路,各种道路间的关系以及道路的特征建立一系列的判据,检查道路的合理性,并产生相应的策略对道路进行反馈处理,实现道路的全自动提取.实验结果表明了该方法的有效性. 相似文献
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基于外延特征的栅格地图噪声去除算法 总被引:5,自引:0,他引:5
为了既能去除彩色城市交通地图的噪声,又能保持有用信息不致损失,在对彩色地图进行像素分类、聚类的基础上,提出了一种基于外延特征的栅格地图噪声去除新算法,该算法首先以所处理的噪声像素为原点建立一个极坐标,再通过8方向搜索来获得噪声像素的外延特征;然后按照“无偏”聚类准则与“有偏”聚类准则,确定该噪声点对道路或区域的新聚类。实验结果表明,该新方法不仅完全去除了地图中的噪声,而且使区域和道路界限分明。 相似文献
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Since maps are widely available for many areas around the globe, they provide a valuable resource to help understand other
geospatial sources such as to identify roads or to annotate buildings in imagery. To utilize the maps for understanding other
geospatial sources, one of the most valuable types of information we need from the map is the road network, because the roads
are common features used across different geospatial data sets. Specifically, the set of road intersections of the map provides
key information about the road network, which includes the location of the road junctions, the number of roads that meet at
the intersections (i.e., connectivity), and the orientations of these roads. The set of road intersections helps to identify
roads on imagery by serving as initial seed templates to locate road pixels. Moreover, a conflation system can use the road
intersections as reference features (i.e., control point set) to align the map with other geospatial sources, such as aerial
imagery or vector data. In this paper, we present a framework for automatically and accurately extracting road intersections
from raster maps. Identifying the road intersections is difficult because raster maps typically contain much information such
as roads, symbols, characters, or even contour lines. We combine a variety of image processing and graphics recognition methods
to automatically separate roads from the raster map and then extract the road intersections. The extracted information includes
a set of road intersection positions, the road connectivity, and road orientations. For the problem of road intersection extraction,
our approach achieves over 95% precision (correctness) with over 75% recall (completeness) on average on a set of 70 raster
maps from a variety of sources.
Yao-Yi Chiang is currently a Ph.D. student at the University of Southern California (USC). He received his B.S. in Information Management from National Taiwan University in 2000 and then his M.S. degree in Computer Science from the USC in December 2004. His research interests are on the automatic fusion of geographical data. He has worked extensively on the problem of automatically utilize raster maps for understanding other geospatial sources and has wrote and co-authored several papers on automatically fusing map and imagery as well as automatic map processing. Prior to his doctoral study at USC, Yao-Yi worked as a Research Scientist for Information Sciences Institute and Geosemble Technologies. Craig A. Knoblock is a Senior Project Leader at the Information Sciences Institute and a Research Professor in Computer Science at the USC. He is also the Chief Scientist for Geosemble Technologies, which is a USC spinoff company that is commercializing work on geospatial integration. He received his Ph.D. in Computer Science from Carnegie Mellon. His current research interests include information integration, automated planning, machine learning, and constraint reasoning and the application of these techniques to geospatial data integration. He is a Fellow of the American Association of Artificial Intelligence. Cyrus Shahabi is currently an Associate Professor and the Director of the Information Laboratory (InfoLAB) at the Computer Science Department and also a Research Area Director at the NSF’s Integrated Media Systems Center at the USC. He received his B.S. in Computer Engineering from Sharif University of Technology in 1989 and then his M.S. and Ph.D. degrees in Computer Science from the USC in May 1993 and August 1996, respectively. He has two books and more than hundred articles, book chapters, and conference papers in the areas of databases, geographic information system (GIS) and multimedia. Dr. Shahabi’s current research interests include Geospatial and Multidimensional Data Analysis, Peer-to-Peer Systems and Streaming Architectures. He is currently an associate editor of the IEEE Transactions on Parallel and Distributed Systems and on the editorial board of ACM Computers in Entertainment magazine. He is also a member of the steering committees of IEEE NetDB and the general co-chair of ACM GIS 2007. He serves on many conference program committees such as VLDB 2008, ACM SIGKDD 2006 to 2008, IEEE ICDE 2006 and 2008, SSTD 2005 and ACM SIGMOD 2004. Dr. Shahabi is the recipient of the 2002 National Science Foundation CAREER Award and 2003 Presidential Early Career Awards for Scientists and Engineers. In 2001, he also received an award from the Okawa Foundations. Ching-Chien Chen is the Director of Research and Development at Geosemble Technologies. He received his Ph.D. degree in Computer Science from the USC for a dissertation that presented novel approaches to automatically align road vector data, street maps and orthoimagery. His research interests are on the fusion of geographical data, such as imagery, vector data and raster maps with open source data. His current research activities include the automatic conflation of geospatial data, automatic processing of raster maps and design of GML-enabled and GIS-related web services. Dr. Chen has a number of publications on the topic of automatic conflation of geospatial data sources. 相似文献
Ching-Chien ChenEmail: |
Yao-Yi Chiang is currently a Ph.D. student at the University of Southern California (USC). He received his B.S. in Information Management from National Taiwan University in 2000 and then his M.S. degree in Computer Science from the USC in December 2004. His research interests are on the automatic fusion of geographical data. He has worked extensively on the problem of automatically utilize raster maps for understanding other geospatial sources and has wrote and co-authored several papers on automatically fusing map and imagery as well as automatic map processing. Prior to his doctoral study at USC, Yao-Yi worked as a Research Scientist for Information Sciences Institute and Geosemble Technologies. Craig A. Knoblock is a Senior Project Leader at the Information Sciences Institute and a Research Professor in Computer Science at the USC. He is also the Chief Scientist for Geosemble Technologies, which is a USC spinoff company that is commercializing work on geospatial integration. He received his Ph.D. in Computer Science from Carnegie Mellon. His current research interests include information integration, automated planning, machine learning, and constraint reasoning and the application of these techniques to geospatial data integration. He is a Fellow of the American Association of Artificial Intelligence. Cyrus Shahabi is currently an Associate Professor and the Director of the Information Laboratory (InfoLAB) at the Computer Science Department and also a Research Area Director at the NSF’s Integrated Media Systems Center at the USC. He received his B.S. in Computer Engineering from Sharif University of Technology in 1989 and then his M.S. and Ph.D. degrees in Computer Science from the USC in May 1993 and August 1996, respectively. He has two books and more than hundred articles, book chapters, and conference papers in the areas of databases, geographic information system (GIS) and multimedia. Dr. Shahabi’s current research interests include Geospatial and Multidimensional Data Analysis, Peer-to-Peer Systems and Streaming Architectures. He is currently an associate editor of the IEEE Transactions on Parallel and Distributed Systems and on the editorial board of ACM Computers in Entertainment magazine. He is also a member of the steering committees of IEEE NetDB and the general co-chair of ACM GIS 2007. He serves on many conference program committees such as VLDB 2008, ACM SIGKDD 2006 to 2008, IEEE ICDE 2006 and 2008, SSTD 2005 and ACM SIGMOD 2004. Dr. Shahabi is the recipient of the 2002 National Science Foundation CAREER Award and 2003 Presidential Early Career Awards for Scientists and Engineers. In 2001, he also received an award from the Okawa Foundations. Ching-Chien Chen is the Director of Research and Development at Geosemble Technologies. He received his Ph.D. degree in Computer Science from the USC for a dissertation that presented novel approaches to automatically align road vector data, street maps and orthoimagery. His research interests are on the fusion of geographical data, such as imagery, vector data and raster maps with open source data. His current research activities include the automatic conflation of geospatial data, automatic processing of raster maps and design of GML-enabled and GIS-related web services. Dr. Chen has a number of publications on the topic of automatic conflation of geospatial data sources. 相似文献
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工程图纸矢量化软件的设计与实现 总被引:1,自引:0,他引:1
工程图纸矢量化是把扫描所得到的光栅图像加以处理、分析、识别,并最终转换成矢量图形格式的过程。矢量化研究是图纸复用、自动理解等应用的基础,是目前CAD领域的一个研究热点。文中介绍了所开发的工程图纸矢量化软件的设计思想和实现技术,包括图像处理和图形自动识别算法,以及为了进一步提高矢量化的准确率而采用的光栅图像编辑和矢量图形编辑等人机交互方法。实验结果证明了以上诸多方法的有效性,软件基本上达到了实用水平。 相似文献
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手工兵棋电子化过程中一个重要的方面就是兵棋地图的数字化,而兵棋地图数字化的基础工作就是地图网格化以及网格定位。在兵棋系统中,为了减小误差,一般采用六角网格覆盖原始地图的方法来实现地图的网格化。在实际推演过程中,作战地图覆盖范围一般很大,那么怎样提高网格化以及网格定位效率就成了地图数字化过程中必须考虑的问题。文章描述了一种效率很高的六边形网格绘制算法,并提出了基于元启发式方法的快速地图网格定位算法,它的时间以及空间复杂都仅有O(1),能够很好的满足兵棋系统中超大地图数字化的要求。 相似文献
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为了更好地进行GIS空间分析,根据GIS应用领域中属性数据的区间值特征,首先利用区间值模糊集来描述模糊属性数据的模糊图层,然后基于区间值模糊集给出了一种栅格图层的模糊叠置分析模型,并改进了基于经典模糊集的模糊叠置分析方法。该模型利用区间值模糊集的基本运算,可以实现普通模糊叠置和加权模糊叠置,而采用区间值,则可以减少属性值模糊性的丢失,且叠置结果符合人们的认知和推理规律,实例结果表明,该模型能够较好地解决区间值属性图层间的模糊叠置分析问题。 相似文献
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1 Introduction Raster to Vector Conversion (RVC) for Engineering Drawings(ED), also known as vectorization, is a procedure to find the vector form of graphic primitives – straight line, circle and arc segments – from the raster engineering drawing. It is an automated process of analyzing and recognizing graphic primitives in the raster engineering drawing, converting them into vector form. A lot of instances need to accomplish the raster to vector recognition and conversion, for example,… 相似文献