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Human beings act mysteriously well on object recognition tasks; they perceive images by sensors and convey information that is processed in parallel in the brain. To some extent, massively parallel computers offer a natural support for similar tasks, since the detection of an object in a scene can be performed by repeating the same operations in different zones of the scene. Unfortunately, most parametric models, commonly used in computer vision, are not very suitable for complex matching operations that involve both noise and severe image distortions. In this paper we discuss an expectation-driven approach for object recognition where, on the basis of the shape of the object to be recognized, we select a few possible zones of the scene where attention will be focused (shape perception): then we examine the previously selected areas, trying to confirm or reject hypotheses of objects, if any (object classification). We propose the use of an architecture that relies on neural networks for both shape perception and object classification. A vision system based on the discussed architectures has been tested on board a mobile robot as a support for its localization and navigation in indoor environments. The obtained results demonstrated good tolerance with respect to both noise and landmark distortions, allowing the robot to perform its task “just-in-time”. The proposed approach has also been tested on a massively parallel architecture, with promising performance. Copyright 1999 Academic Press

In this work a system is presented, provided with a flexible natural language interface integrated with interactive graphics. A user can interact with the system through two channels: i) world modelling system (WMS), ii) natural language interface (NLI). WMS allows definition of physical objects by means of a Concept Description Language (CDL) and storage of their properties in a Conceptual Data Base (CDB) as well as execution of graphic and geometric operations on defined objects (GL, graphic language) GL can also be accessed by the NLI by means of declarative sentences. NLI is a general purpose tool, which can be configured by the user for a specific application domain modelling interactively the semantics of the italian language. The application domain considered in this work is the synthesis of scenographies.