Object Localization: Selection of Optimal Reference Objects

The quality of an object localization depends essentially on the adequate selection of a suitable reference. In most computational approaches developed so far only the distance between the located object and a potential reference object has been used as a decision criterion. However many other criteria have to be considered for a cognitive plausible selection of adequate reference points. In this paper we investigate how object and context dependent properties, like referentiality, visual salience, functional dependencies, or prior knowledge, influence the quality of a reference object. Each factor is quantitatively determined and scaled by relevance to a certain context. The scaling permits the necessary comparability of the different quality criteria. Finally, on the basis of these factors a computational model is presented which permits a context dependent determination of the optimal reference object in a particular spatial configuration

An Empirically Validated Model for Computing Spatial Relations

In the last couple of decades increasingly sophisticated models for computing spatial relations have been developed. The approaches have mostly been based on introspection and have not been validated for their correctness. We therefore designed experimental studies to verify the crucial hypotheses of a proposed computational model for establishing spatial relationships between extended objects in 2D and 3D space. The main point of interest was to clarify the dependencies between angle, distance and shape when establishing projective relations. It appeared that the angular deviation plays the key role when applying relations of this class. The degree of deviation was dependent upon the extension of the reference object. After slight adjustments of the algorithm to the empirical outcome we were able to predict the experimental results and therefore to validate the proposed model

Angle, Distance, Shape, and their Relationship to Projective Relations

The semantics of spatial relations have been intensively studied in linguistics, psychology, and cognitive neuroscience. Angle, distance, and shape are widely considered to be the key factors when establishing spatial relations. In this work an empirical study shows that previous theories overemphasizevariation and we clarify the interdependencies between angle, distance, and shape with respect to the acceptability of projective relations. It turned out that the angular deviation plays the key role for relations of this class. The degree of deviation was dependent upon the extension of the reference object perpendicular to the canonical direction of the relation. There was no major effect due to the distance. However, distance interacted with the angular deviation if the located object was very close to the reference object. The experimental results can now be used as a theoretical framework for validating existing computational models of projective relations for their cognitive plausi..

Basic Meanings of Spatial Relations: Computation and Evaluation in 3D Space

Spatial relations play an important role in the research area of connecting visual and verbal space. In the last decade several approaches to semantics and computation of spatial relations in 2D space have been developed. Presented here is a new approach to the computation and evaluation of basic spatial relations' meanings in 3D space. We propose the use of various kinds of approximations when defining the basic semantics. The vagueness of the applicability of a spatial relation is accounted for by a flexible evaluation component which enables a cognitively plausible continuous gradation. For validating the evolved methods we have integrated them into a workbench. This workbench allows us to investigate the structure of a spatial relation's applicability region through various visualization methods

From Vision to Language: A Cognitive Approach to the Computation of Spatial Relations in 3D Space

Research on connecting visual and verbal space not only results in a better understanding of the human cognitive abilities relating to visual perception and spatial processing but also opens up promising possibilities for the development of industrial applications. After discussing some examples, the paper concentrates on the computation of spatial relations, which play a key role in the translation of visual information into natural language. The use of a multilevel semantic model is proposed to define the semantics of spatial relations. The model distinguishes, with respect to geometric and linguistic aspects, the context-specific conceptual knowledge from the basic meanings of the spatial relations. In contrast to most of the existing approaches, which consider only the 2D case, a new approach to the computation and evaluation of the semantics of spatial relations in 3D space is presented. The use of several kinds of idealizations for defining the basic semantics of spatial relation..

Processing Spatial Relations in Object Localization Tasks

The localization of an object within a certain environment requires the selection of an appropriate reference object and the establishment of adequate spatial relations between the objects. The decision, which reference object to choose, demands consideration of all applicable spatial relations, which therefore have to be computed before the final decision can be made. However, the calculation of all applicable spatial relations for all potential reference objects is a computationally intensive task. In order to increase performance, it is proposed that the set of spatial relations to be tested can be reduced in two respects without any loss of quality in the generated answer. The two ways in which reduction can be achieved are by taking into account the type of the localization --- approximate or precise --- and the type of the reference object

Octree-Based 3D Logic and Computation of Spatial Relationships in Live Video Query Processing

Live video computing (LVC) on distributed smart cameras has many important applications; and a database approach based on a Live Video DataBase Management System (LVDBMS) has shown to be effective for general LVC application development. The performance of such a database system relies on accurate interpretation of spatial relationships among objects in the live video. With the popularity of affordable depth cameras, 3D spatial computation techniques have been applied. However, the 3D object models currently used are expensive to compute, and offer limited scalability. We address this drawback in this article by proposing an octree-based 3D spatial logic and presenting algorithms for computing 3D spatial relationships using depth cameras. To support continuous query processing on live video streams, we also develop a GPU-based implementation of the proposed technique to further enhance scalability for real-time applications. Extensive performance studies based on a public RGB-D dataset as well as the LVDBMS prototype demonstrates the correctness and efficiency of our techniques