面向对象的扫描地形图自动分层算法研究

地形图的数字化对构建地理信息系统有十分重要的意义。借鉴面向对象的思想,提出了一种扫描地形图自动分层算法,算法先将地形图线划要素与普染区背景分离;然后把具有相同特征的像素分割成线段对象与块对象,以这些对象为最基本的分类单元;通过提取对象的颜色、形状和近邻关系等特征,建立分类标准,实现地形图的自动分层。该算法能有效克服扫描引起的颜色混淆和假彩色的影响,分层结果完整连通,从而很大程度上抑制“椒盐”现象的发生。     

基于Guided Filter的地形图中线要素提取算法

针对彩色扫描地形图中线要素与背景要素难以分离,现有线要素提取算法提取的结果中存在边界不准确的问题,提出基于Guided Filter的地形图中线要素提取算法。利用基于能量密度和Shear变换相结合的线要素提取算法对线要素进行粗提取;引入Guided Filter,以源地形图图像作为Guided Filter的引导图像,以粗提取的线要素图像作为Guided Filter的输入图像,经过滤波处理获得的线要素信息更为显著;利用OTSU算法得到最终的线要素信息。实验结果表明：相对于现有的线要素提取算法,提出的算法能够更为准确地提取出地形图中的线要素,并具有更好的噪声抑制能力。     

基于聚类与SIFT算法的自动地形成图方法研究

针对智能水下机器人由于长时间工作,在进行地形图构建时,成图精度变差的问题,提出了一种基于聚类和尺度不变特征转换算法的,可以对相邻测线的地形图进行自动成图修正的新方法。利用尺度不变特征转换算法和K-means算法进行特征区域的提取与配对,再利用卡尔曼滤波算法进行目标跟踪,即可得到较为准确的坐标数据,从而提高地形图精度。试验证明该方法能够较为准确地进行特征点的选择以及配对,并进行目标跟踪,将误差降低56.15%。实现了相邻测线的自动修正和地形成图,具有可行性。     

一种地形图粘连汉字提取算法

地形图中包含了大量的字体丰富的汉字注记,其中有一部分由于与其它图符对象相互粘连而使得对象的尺寸变大超过了预定的阈值或失去了原有的结构特性,大大增加了提取难度。本文提出了一种寻求最佳分割点的算法来去除粘连,提取汉字的算法,取得了良好的效果。首先,在已提取出的汉字周围确定一个局部搜索范围,当局部范围内存在大尺寸的对象时表明可能有潜在的粘连汉字;其次,以图象中的分枝点和端点为顶点,以其中的图段为边建立对应的图;然后,在图中寻找最佳分割点,将图符分割成不同的互相分离的几个部分;最后,用连通成分结构分析的方法来提取汉字。     

地形图数字注记的自动提取与识别

地形图的自动扫描矢量化是GIS领域亟待解决的一个重要难题。地形图中包含了大量的字体丰富的数字注记,用以表示地物地貌的属性等特征,正确提取并识别这些数字是图纸处理中的重要组成部分。本文分析了现有的提取方法的不足,提出了一种新的数字注记自动提取与识别算法,首先根据先验的尺寸大小确定候选数字,再采用OCON结构的BP神经网络识别出真正的数字,然后利用近邻关系提取出扩展数字。实验表明,该算法是快速、高效、可靠的。     

地形图点状符号的自动提取和识别

针对扫描地形图的点状符号, 提出了一种基于距离变换进行提取和识别的新方法. 首先优化分版图的提取结果, 根据弯曲密度和线划密度过滤掉非点状符号的线划, 利用加壳变换和蜕皮变换进行点状符号的图文分离. 然后根据加权距离函数来度量符号的全局特征, 由骨架线的一致性来度量符号的几何形状特征, 并结合这两个特征来识别点状符号. 最后, 提出了错切文字注记和线状要素注记的识别方法.     

地图自动输入系统中快速输入等值线群的算法

研究地图中等值线群的快速输入问题,给出了自动跟踪提取等值线群的有效算法。该算法在跟踪提取各圈等值线的过程中顺以脱离人工干预,能自动识别并处理相邻等值线的碰撞问题,自动弥合等值线上的缺口。     

一种光栅图像地图转化成矢量数据地图的方法

矢量化光栅地图是一个重要而又远未解决的问题。该文通过对分布式虚拟战场环境中仿真实体及其应用研究的开发应用,对光栅图像地图转化成矢量数据地图的方法进行研究。该方法首先对扫描仪扫描输入的图像进行处理,然后采用一种动态线划的自动、半自动跟踪算法,数字化现有地图上的等高线等地貌信息,该方法是利用跟踪地图地理要素线划中心的模式完成跟踪的,较好地模拟了人工矢量化所采用的策略和方法,获得的矢量数据骨架点准确,失真少、处理速度快,与人的直观视觉相符,能用于各类地图的矢量化。     

基于方框邻域内点的等高线识别方法研究

文章以扫描输入的大比例彩色地形图为研究对象,重点讨论了彩色地形图中等高线的计算机识别方法,提出了基于方框邻域内点的算法,针对分色后提取出来的等高线二值图像进行识别和处理,使等高线中的首曲线和计曲线区分开来,以达到计算机可以识别的目的。同时该算法和思想还具有通用性,可以推广到其它有关曲线图像粗细识别中去。     

基于可变形模型及区域流向分析的等高线自适应矢量化算法

等高线的矢量化一直是地形图自动识别的难点之一．该文对目前图纸矢量化算法进行了总结分析,根据等高线本身所具有的特征,结合人眼对等高线跟踪提取的特点,提出可变形模型跟踪矢量化算法．算法通过初始种子段的生成、可变内力控制及区域流向分析,直接在原地形图上实现等高线的自动矢量化及断点、粘连点的处理．实验证明该方法行之有效且具有很好的稳定性．     

电缆网电路图自动布图算法的研究与系统实现

针对电缆网电路图的自动生成问题,在借鉴传统电路原理图自动布图的行列布局与通道布线思想的基础上,同时结合电缆网电路图的特点,提出了先位号行定位,再点号列定位的自动布局算法,并在两相邻位号间形成的行通道与相邻点号间形成的列通道间进行通道布线。算法成功应用于自主研发的电缆网电路图自动布图系统。系统能够根据电缆网表中的元器件连接信息,自动生成逻辑功能正确且布图美观规范的电缆网电路图,系统已被多个航天研究所在火箭、导弹的电缆网设计工作中采用,显著提高了电缆网设计工作的自动化程度。     

MAGELLAN: Map Acquisition of GEographic Labels by Legend ANalysis

A system named MAGELLAN (denoting Map Acquisition of GEographic Labels by Legend ANalysis) is described that utilizes the symbolic knowledge found in the legend of the map to drive geographic symbol (or label) recognition. MAGELLAN first scans the geographic symbol layer(s) of the map. The legend of the map is located and segmented. The geographic symbols (i.e., labels) are identified, and their semantic meaning is attached. An initial training set library is constructed based on this information. The training set library is subsequently used to classify geographic symbols in input maps using statistical pattern recognition. User interaction is required at first to assist in constructing the training set library to account for variability in the symbols. The training set library is built dynamically by entering only instances that add information to it. MAGELLAN then proceeds to identify the geographic symbols in the input maps automatically. MAGELLAN can be fine-tuned by the user to suit specific needs. Recognition rates of over 93% were achieved in an experimental study on a large amount of data. Received January 5, 1998 / Revised March 18, 1998     

彩色图件自动分层算法研究

在彩色图件输入识别研究领域中,通常采用颜色分层的方法来降低图件识别的难度,文中基于瘢频数直方图和颜色表,分别提出了两种自动分层算法,结果表明,算法不仅可以处理单颜色背景的图像而且可以处理多颜色背景的图像,前者具有较强的鲁棒性,后者具有较经的实时性,实际应用效果良好。     

一种改进的OFDM符号定时同步算法

针对先前一些算法存在定时模糊以及干扰峰峰值较大的问题,提出了一种改进的OFDM符号定时同步算法。分析了先前算法问题产生的根源,设计了两种同时具备频偏估计能力的新训练符号,并给出了相应的定时尺度函数。对新算法的定时尺度函数进行了分析,得知它的定时尺度非常尖锐,几乎不存在干扰峰,而且不受循环前缀长度的影响。仿真结果表明新算法克服了已有算法的缺点,有效地提高了定时估计的精度。当信噪比大于5dB,在加性白高斯噪声信道下定时估计的均值与方差都为零,可以实现精确的定时同步。     

基于SVG的矢量WcbGIS地图符号库的构建与引用

SVG提供了强大的空间数据表达能力,因此可基于SVG构建WebGIS地图中所需的不同比例尺下的各种地图符号,最终组建地图符号库,以供WebGIS实现不同地图时进行引用,从而减少WebGIS中构建地图时不必要的重复工作。在基于SVG的矢量WebGIS中,地图符号可实现一次定义,多次引用,增加新的地图符号只是在不同的位置增加一个地图符号的引用而已,而且,在引用时可指定地图符号在地图中出现的位置、地图符号的颜色及线宽等。通过引用相同的预定义的地图符号,在不增加实际的绘图数据的情况下减少了数据量;同时,如果要修改某类信息的所有的地图符号,只要修改预定义的地图符号即可,无需逐一修改,方便了管理,而且由于地图符号都是矢量的,减少了对存储空间和网络带宽的要求,加快了地图的显示。     

Parametric graph drawing

A diagram is a drawing on the plane that represents a graph like structure, where nodes are represented by symbols and edges are represented by curves connecting pairs of symbols. An automatic layout facility is a tool that receives as input a graph like structure and is able to produce a diagram that nicely represents such a structure. Many systems use diagrams in the interaction with the users; thus, automatic layout facilities and algorithms for graphs layout have been extensively studied in the last years. We present a new approach in designing an automatic layout facility. Our approach is based on a modular management of a large collection of algorithms and on a strategy that, given the requirements of an application, selects a suitable algorithm for such requirements. The proposed approach has been used for designing the automatic layout facility of Diagram Server, a network server that offers to its clients several facilities for managing diagrams     

地图符号服务与地图服务的耦合

为了实现地图符号在地图服务(WMS)中的应用,达到灵活定制WMS可视化效果的目的,从支持WMS的图层样式描述规范(SLD)入手,通过对SLD和WMS的分析,研究网络地图符号的发布方法,设计地图符号服务框架,提出了基于SLD,地图符号服务与WMS的耦合方法,以解决基于WMS的WebGIS应用中无法通过符号改变地图可视化效果的问题,实验结果表明该方法可以有效地关联地图符号服务与WMS,实现了WMS可视化效果的灵活定制.     

A language independent error recovery method for LL(1) parsers

An efficient and systematic LL(1) error recovery method is presented that has been implemented for an LL(1) parser generator. Error messages which provide good diagnostic information are generated automatically. Error correction is done by discarding some input symbols and popping up some symbols from the parsing-stack in order to restore the parser to a valid configuration. Thus, symbol deletions and insertions are simulated. The choice between different possible corrections is made by comparing the cost of the inserted (popped) symbols with the reliability value of the recovery symbol (the first input symbol that is not discarded). Our concept of reliability is based on the observation that input symbols differ from each other in their ability to serve as recovery points. A high reliability value of a symbol asserts that it was probably not placed in the input by accident. So it is reasonable not to discard that symbol but to resume parsing. This is done even if a string with high insert-cost has to be inserted before that symbol in order to fit it to the part of the program that has already been analysed. The error recovery routine is invoked only when an error is detected. Thus, there is no additional time required for parsing correct programs. Error-correcting parsers for different languages, including Pascal, have been generated. Some experimental results are summarized.     

On the emergence of rules in neural networks

A simple associationist neural network learns to factor abstract rules (i.e., grammars) from sequences of arbitrary input symbols by inventing abstract representations that accommodate unseen symbol sets as well as unseen but similar grammars. The neural network is shown to have the ability to transfer grammatical knowledge to both new symbol vocabularies and new grammars. Analysis of the state-space shows that the network learns generalized abstract structures of the input and is not simply memorizing the input strings. These representations are context sensitive, hierarchical, and based on the state variable of the finite-state machines that the neural network has learned. Generalization to new symbol sets or grammars arises from the spatial nature of the internal representations used by the network, allowing new symbol sets to be encoded close to symbol sets that have already been learned in the hidden unit space of the network. The results are counter to the arguments that learning algorithms based on weight adaptation after each exemplar presentation (such as the long term potentiation found in the mammalian nervous system) cannot in principle extract symbolic knowledge from positive examples as prescribed by prevailing human linguistic theory and evolutionary psychology.