非连通空间对象方向关系表达与推理

Description and reasoning with direction relation of disconnected spatial objects

为了弥补现有的二维空间对象方向关系表达模型大都利用点、最小外包矩形等近似地代替空间对象,距离真实空间对象间方向关系的描述与推理仍存在差距的不足,提出了一种基于Voronoi图的非连通空间对象方向关系表达模型。该模型借助Gestalt心理学理论,通过提取非连通空间对象的特征点、特征链,构建空间对象间的可视区域,生成方向关系Voronoi图,实现了非连通、含洞的参考对象与目标对象间方向关系的表达。该模型较好地顾及了空间对象形状、大小等因素带来的影响,表达精度更高、适用范围更广。为了提高复杂空间对象方向关系复合推理的精度,基于该模型提出了一个非连通对象间主方向关系复合推理算法。该算法借助Tile-union运算和Pr运算,实现了该模型下基本主方向关系的复合推理,降低推理结果的不确定性。分析和验证的结果表明,提出的非连通空间对象方向关系模型及复合推理算法,提高了表达与推理的精度,完善和提高了对复杂空间对象方位关系的分析与处理能力。

In view of the fact that there is still a certain gap in representing and reasoning with the orientation relation between real objects due that the existing models for representing and reasoning with direction relations use a point and minimum bounding rectangle to approximate the spatial object itself,this paper proposed a new model for dealing with the disconnected spatial objects based on Voronoi diagram.With the help of the theory of Gestalt psychological,the proposed model constructed visual regions between spatial objects by extracting the feature points and feature chains of the disconnected spatial objects to generate the direction Voronoi diagram,which realized the representation of directions between the space objects which were disconnected and contain holes.This model fully considered the influence of the shape,size and other factors of the spatial objects,and had higher expression accuracy and wider applicability.In order to improve the precision of reasoning with cardinal directional relations between complex spatial objects,this paper proposed an algorithm for composing cardinal direction relations defined by the proposed model,which reduced the uncertainty of the results of composition by means of Tile-union operation and Pr operation.The results of analysis and verification show that the proposed model and algorithm reduce the uncertainty of the reasoning results,improve the accuracy of representation and reasoning,and then enhance the ability to analyze and process the direction relations with complex spatial objects.