EFFICIENT LEARNING OF VAM-BASED REPRESENTATION OF 3D TARGETS AND ITS ACTIVE VISION APPLICATIONS

There has been a considerable interest in using active vision for vari ous applications. This interest is primarily because active vision can enhance machine vision capabilities by dynamically changing the camer a parameters based on the content of the scene. An important issue in active vision is that of representing 3D targets in a manner that is i nvariant to changing camera configurations. This paper addresses this representation issue for a robotic active vision system. An efficient Vector Associative Map (VAM)-based learning scheme is proposed to lear n a joint-based representation. Computer simulations and experiments a re first performed to evaluate the effectiveness of this scheme using the University of Illinois Active Vision System (UIAVS). The invarianc e property of the learned representation is then exploited to develop several robotic applications. These include, detecting moving targets, saccade control, planning saccade sequences and controlling a robot m anipulator. (C) 1998 Elsevier Science Ltd. All rights reserved.