Towards the Development of a Multisensorial Vision System: An Adaptive Approach to the Low-Level Phase

The use of vision systems in order to accomplish the task of recognizing and describing 3D outdoor scenes is an open problem faced by current research on Computer Vision. In the past few years new methodologies have been developed which make it possible to integrate different information sources about a given surrounding environment into a single recognition framework. The system proposed in this paper has been designed to reach an high degree of reliability, by adaptively integrating data coming from a variable number of physical and virtual imaging sensors. The problem addressed in this paper lies in optimizing the choice of low-level methods for processing the outputs of this set of sensors, and for extracting data that better describe, at a symbolic level, the scene under examination. The system, which integrates techniques typical for Pattern Recognition, Artificial Intelligence, and Image Processing is characterized by a modular structure and has the capability for evaluating the data and results obtained during the whole recognition process. To this end attention has been focused on correlating the symbolic and numerical aspects of the computational process; consequently, it has been made possible to assess the progressive integration results according to accurate evaluation criteria.