混合维拓扑和尺寸关系的定性空间推理

定性空间推理(QSR)研究空间关系,多数工作集中在单维空间关系,但在地理信息系统(GIS)中多维对象很常见.混合维对象空间关系是指点、线和区域3类对象出现在同一场景的情况,该类问题对定性空间推理研究有着重要的理论意义和应用价值.但这方面的研究工作还比较少.在已有的混合维区域连接演算的基础上进行完善,提出了MRCC5混合维拓扑模型,并研究了其上约束满足推理问题的复杂度.对定性尺寸关系进行了混合维扩展.给出了MDS模型,进而研究了其推理问题.在以上工作基础上.提出了RCCA和MDS的结合模型,给出并分析了结合模型的推理算法.将定性空间推理相关研究推广到混合维领域,深入研究了混合维拓扑关系推理,提出了混合维尺寸以及混合维拓扑尺寸结合模型.     

定性空间推理的分层递阶框架

定性空间推理是定性推理和空间推理的重要组成部分 .拓扑和形状是定性空间推理研究的关键问题 .针对定性空间推理已有一般框架存在的问题 ,提出了定性空间推理的分层递阶框架 ,并结合拓扑和形状方面的定性空间推理研究工作阐述了所提出的框架的有效性和合理性 .最后总结了分层递阶框架的要点并提出了基于该框架的进一步研究工作 .     

近似空间关系代数ASRA及应用

粗定位模型是一种基于粗集的近似区域表示模型 ,基于定性空间推理理论对其进行了代数形式化 .通过空间关系矩阵和 2 4 9种基本空间关系构造了近似空间关系代数 ASRA;讨论了 ASRA的公理和基本性质 ,研究了ASRA和 RCC5关系映射中存在的不确定性 ;把 ASRA应用于 GIS,提出了基于 ASRA的空间关系判定算法ASRA- RCC.与同类算法相比 ,ASRA- RCC能够同时支持确定和近似区域 ,并且具有较高的效率     

空间方向关系描述模型及其GIS应用分析

空间方向关系建模是一个属于空间认知范畴的研究问题。近20年来,该研究问题受到来自计算机、人工智能、机器人以及地理信息科学等领域的众多学者们关注,提出了许多形式化描述模型。首先阐述了方向关系描述框架、表达形式、基本性质以及影响方向关系描述的主要因素。然后,重点回顾及评价了一些较有代表性的方向关系建模方法及其在GIS空间查询、分析、推理中的应用,指出了其中存在的一些主要问题。最后,展望了方向关系模型及其应用中有待进一步研究的若干相关工作。     

定性方向关系模型研究进展

空间关系形式化模型的发展是空间推理、地理信息系统(GIS)、机器人导航等领域的一个非常重要的研究内容,近年来受到相关领域研究者的极大重视。空间对象的方向关系模型的研究已经取得了一定的进展。本文介绍了近年来空间对象的方向关系形式化模型的主要研究内容、研究方法和研究进展,对已有的方向关系模型做了比较,并探讨了目前存在的问题和今后的发展方向。     

基于缓冲区的扩展拓扑关系模型及应用

定性空间推理在人工智能等领域有着广阔的应用前景,但目前单方面空间关系研究较多,多方面结合研究较少,这与实际应用需求不符.由于各类空间关系具有独立性,需要找到适当的理论将它们融合,目前对于拓扑、距离结合模型的研究还不够充分.针对缺乏基本关系可处理且易于在GIS系统中实现的模型等情况,提出了一种扩展拓扑关系模型BERCC.BERCC源于RCC理论,其主要思想是通过考虑缓存区之间的拓扑关系来提高模型表达能力,同时能表达一定程度的距离信息.推导了BERCC的弱复合表,证明了BERCC基本关系是可处理的,给出了一个包括全集关系和基本关系的可处理子集,在此基础上实现了约束满足推理算法.最后,基于该理论和方法实现了一个实验系统,进一步验证了模型及算法的正确性和实用性.     

方位关系约束满足问题的推理求解

约束满足问题（Constraint Satisfaction Problems CSP）是人工智能的一个研究领域,诸如空间查找、规划等问题都可转化为约束满足问题。方位关系是空间关系的重要组成部分,用以确定空间对象间的一种顺序。本文研究了空间方位关系模型,给出了方位关系约束的一般表示形式。在此基础上,利用组合表推理给出了方位关系约束满足问题的一个推理求解算法,该算法的时间复杂度为O（n^2）。     

Shape understanding system: Understanding the thin object

This paper presents the method of understanding objects that can be considered as thin objects. The proposed method of understanding thin objects is part of the shape understanding method developed by the author. The main novelty of the presented method is that the process of understanding thin objects is related to the visual concept represented as a symbolic name of the possible class of shapes. The possible classes of shape, viewed as hierarchical structures, are incorporated into the shape model. At each stage of the reasoning process that led to assigning of an examined object to one of the possible classes, novel processing methods are used. These methods are very efficient because they deal with a very specific class of shapes. In this paper, the 2-D objects that are classified as thin objects are regarded as geometrical objects without any reference to the real world objects. However, the shape under standing method is designed to understand an object at many levels of interpretation, such as the topological level, the linguistic level and the real world reference level. This approach influences the way in which the system of shape understanding is designed. The system consists of different types of experts that perform different processing and reasoning tasks.     

基于方向关系组合运算表的定性推理

方向关系的定性推理是GIS中的一个重要的理论问题。本文介绍基于井字空间的方向关系定性表示。着重讨论基于方向关系组合运算表的定性推理，最后给出一个基于方向关系组合运算表的定性推理算法。     

Topological Spatio–Temporal Reasoning and Representation

We present here a theory of motion from a topological point of view, in a symbolic perspective. Taking space–time histories of objects as primitive entities, we introduce temporal and topological relations on the thus defined space–time to characterize classes of spatial changes. The theory thus accounts for qualitative spatial information, dealing with underspecified, symbolic information when accurate data are not available or unnecessary. We show that these structures give a basis for commonsense spatio–temporal reasoning by presenting a number of significant deductions in the theory. This can serve as a formal basis for languages describing motion events in a qualitative way.     

A Statistical Model for Directional Relations Between Spatial Objects

Directional relation, as a kind of spatial constraints, has been recognized as being an important means for spatial query, analysis and reasoning. Directional relation is conventionally concerned with two point objects. However, in spatial query and analysis, there is also a need of directional relations between point and line, point and area, line and line, line and area, and area and area. Therefore, conventional definition of direction needs to be extended to include line and area objects (i.e. the so-called extended objects). Existing models for directional relation of extended objects make use of approximate representations (e.g. minimum bounding rectangles) of the extended objects so as to produce some results with unrealistic impression. In this paper, a statistical model is presented. In this new model, (1) an extended spatial object is decomposed into small components; (2) the directional relation between extended spatial objects is then determined by the directions between these small components which form a distribution; and (3) two measures (i.e. range and median direction) are utilized to describe the statistical property of the distribution. This statistical model is based upon the (extended) spatial objects themselves, instead of their approximate representations. An experimental test has been carried out and the result indicates that the directional relations computed from this model is very close to those perceived by human beings.

Multi-granularity and metric spatial reasoning

Composition reasoning is a basic reasoning task in qualitative spatial reasoning (QSR). It is an important qualitative method for robot navigation, node localization in wireless sensor networks and other fields. The previous composition reasoning works dedicated in single granularity framework. Multi-granularity spatial relation is not rare in real world, and some qualitative spatial relation models are multi-granularity models, such as RCC, STAR_m, CDC_m and OPRA_m. Although multi-granularity composition reasoning is very useful in many applications, it has not been systematically studied before. A special case of multi-granularity composition reasoning, referred to as metric spatial reasoning, is also discussed here. The general frameworks and basic theories for multi-granularity and metric spatial reasoning are put forward here. Furthermore, we redefine the spatial relation models for distance, topology and direction under the proposed multi-granularity and metric frameworks. We add metric representation for the OPRA_m. The multi-granularity and metric reasoning tasks are studied for these four models for the first time. Finally we perform some experiments on OPRA_m with encouraging results to verify our theories. Multi-granularity and metric spatial reasoning tasks are new problems in QSR and quite different from the previous works. Our works can be potentially applied in robot navigation, wireless sensor networks and other applications.     

Content-based Access in Image Database by Quantitative Relationships

In this paper, we describe a novel technique to perform content-based access in image databases using quantitative spatial relationships. Usually, spatial relation-based indexing methods fail if the metric spatial information contained in the images must be preserved. In order to provide a more robust approach to directional relations indexing with respect to metric differences in images, this paper introduces an improvement of the virtual image index, namely quantitative virtual image, using a quantitative methodology. A scalar quantitative measure is associated with each spatial relation, in order to discriminate among images of the image database having the same objects and spatial relationships, but different degree of similarity if we also consider distance relationships. The measure we introduce does not correspond to any significant increase of complexity with respect to the standard virtual image providing a more precise answer set.     

利用线画图象二维串的图象理解方法

本文提出一种基于符号运算和面向规则线画图象的自动图象理解方法，方法用代数符号表达空间物体，用两个符号串描述物体在特定的投影空间上的相互关系，上方法所构造的代数系统规定了包括微分，腐蚀等等代数操作定义了若干条运算规则。利用这些操作和规则，可以实现对图象中物体的空间位置，运动趋势，自身大小的变化等等的自动分析。     

How qualitative spatial reasoning can improve strategy game AIs

Spatial reasoning is a major challenge for strategy-game artificial intelligence systems. Qualitative spatial reasoning techniques can help overcome this challenge by providing more expressive spatial representations, better communication of intent, better path-finding and reusable strategy libraries.     

TOUR及其改进模型在路径搜索中的应用研究

定性空间推理是近几年来空间关系推理的一个热点,TOUR模型就是一个很好的定性空间推理模型,它基于认知图,对空间信息进行合理描述,能够吸收新的信息和解决路径搜索问题,但TOUR模型本身也存在一些不足之处。文中介绍了基于空间关系路径搜索的TOUR模型,用TOUR模型解决了一个具体的路径搜索问题,根据具体的应用指出了TOUR模型的不足,对TOUR模型进行了改进,并提出了改进后的算法,最后分析了对TOUR模型改进后的主要优势。     

定性空间推理中区域连接演算的多维扩展

区域连接演算(RCC)是定性空问推(QSR)的基础理论之一．但RCC理论只支持区域，不能处理包括点、线和区域在内的空问多维对象，这阻碍了RCC应用的发展．扩展了区域概念，将点和线对象视为特殊的区域．提出了能直接用RCC理论描述空间多维对象拓扑关系的MRCC理论．在保留RCC公理的前提下，MRCC增加了2条新公理，并由此推导出了36种MRCC基本关系．进而讨论了基于概念邻域图和复合表的推理．MRCC拓展了RCC理论的适用范围，促进了RCC向实际应用的发展．     

基于基因搜索的模糊图象解释方法

图象解决是计算机视觉的重要组成部分，它涉及图象处理，分类器设计和逻辑推理等许多领域。针对目前图象解释系统要面对的严重噪声、模糊性和不确定性问题。重点研究了一种基于基因搜索的双向推理技术，该算法分为如下两步：首先通过基于分割区域统计/几何特征的模式分类器来得到初始的分类模糊隶属度，并根据经验（或统计）得到的先验空间位置关系模糊规则来构造一种有效表达图象解释信息的模糊图。然后通过基因搜索算法融合上面的两类信息来得到图象的最佳解释，实验结果表明，该方法对具有单一对象或多个对象的区域均有很好的效果，也是对基于概率、证据和模糊推理等单向推理机制图象解释方法的提高。