Representation and fusion of heterogeneous fuzzy information in the 3D space for model-based structural recognition—Application to 3D brain imaging

AbstractWe present a novel approach to model-based pattern recognition where structural information and spatial relationships have a most important role. It is illustrated in the domain of 3D brain structure recognition using an anatomical atlas. Our approach performs segmentation and recognition of the scene simultaneously. The solution of the recognition task is progressive, processing successively different objects, and using different pieces of knowledge about the object and about relationships between objects. Therefore, the core of the approach is the knowledge representation part, and constitutes the main contribution of this paper. We make use of a spatial representation of each piece of information, as a spatial fuzzy set representing a constraint to be satisfied by the searched object, thanks in particular to fuzzy mathematical morphology operations. Fusion of these constraints allows us to select, segment and recognize the desired object

© 2000 Kluwer Academic Publishers. Printed in the Netherlands. Using fuzzy sets to represent uncertain spatial knowledge in autonomous robots ∗

Abstract. Autonomous mobile robots need the capability to reason from and about spatial knowledge. Due to limitations in the prior information and in the perceptual apparatus, this knowledge is inevitably affected by uncertainty. In this paper, we discuss some techniques employed in the field of autonomous robotics to represent and use uncertain spatial knowledge. We focus on techniques which use fuzzy sets to account for the different facets of uncertainty involved in spatial knowledge. These facets include the false measurements induced by bad observation conditions; the inherent noise in odometric position estimation; and the vagueness introduced by the use of linguistic descriptions. To make the discussion more concrete, we illustrate some of these techniques showing samples from our work on mobile robots. Key words: environment modeling, fuzzy logic, linguistic descriptions, robot navigation, self localization, spatial maps, uncertainty managemen