Fuzzy Object Localization Based on Directional (and Distance) Information

Abstract

A directional spatial relationship to a reference object (e.g., "east of the post office") can be represented by a spatial template, i.e., a fuzzy subset of the Euclidean space. For each point of the space, the template indicates to what extent the relationship holds. The objects for which the relationship holds best can then be located. In previous work, we discussed the case of crisp 2D objects in raster form. We introduced a new algorithm for directional spatial template computation, which is faster, gives better results and is more flexible than its competitors. The present paper continues this line of research. The algorithm is extended to handle fuzzy objects and embed distance information. In existing models, only angular deviation is taken into account. Spatial distance, however, also contributes in shaping directional templates.