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Ontology-driven scene interpretation based on qualitative spatial reasoning

机译:基于定性空间推理的本体驱动场景解释

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In this paper we present an approach to interpret composite objects in a scene and label them as members of a particular ontological feature class according to the spatial arrangements of their components. As these arrangements are often indicators of the functional role of the corresponding components, they can be viewed as spatial signatures for such features, and their analysis offers a strong lead for scene interpretation and labeling. In order to pursue this objective, we need efficient quantitative metrics to describe the spatial arrangements, and for that purpose we adopt the Histogram of Forces technique and fuzzy Allen-derived descriptions of spatial relations. This leads to the generation of pairwise metric expressions describing the spatial relations among individual components of a composite object. Such pairwise expressions can be aggregated to describe the overall spatial arrangement of an object’s components. Such layout tables of different objects can be compared through an assessment of their normalized crosscorrelation, in order to compare their spatial layouts and decide whether they belong to the same feature class. Through this approach we are merging principles from scene interpretation, similarity assessment, and ontology to advance our capability to understand complex scenes. In this paper we present the components of our approach and also provide an application using three different classes of airports, to demonstrate its performance.
机译:在本文中,我们提出了一种解释场景中的复合对象的方法,并根据其组件的空间布置将它们标记为特定的本体特征类的成员。由于这些安排通常是相应组件的功能作用的指示器,因此可以被视为这些特征的空间签名,并且它们的分析为场景解释和标签提供了强大的铅。为了追求这一目标,我们需要有效的定量指标来描述空间安排,为此目的,我们采用了力量技术的直方图和模糊艾伦派生的空间关系描述。这导致生成描述复合对象的各个组件之间的空间关系的成对度量表达式。可以聚合这种成对表达以描述对象组件的总空间布置。可以通过评估其归一化横相关来比较不同对象的这种布局表,以便比较它们的空间布局并决定它们是否属于同一特征类。通过这种方法,我们正在合并现场解释,相似性评估和本体的原则,以提高我们理解复杂场景的能力。在本文中,我们介绍了我们的方法的组成部分,并提供了使用三种不同类别的机场的应用,以证明其性能。

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