Efficient Learning of VAM-Based Representation of 3D Targets and its Active Vision Applications

• Published in: Neural networks Vol. 11; no. 1; pp. 153 - 171
• Main Authors: Srinivasa, Narayan, Sharma, Rajeev
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 1998; Elsevier Science
• Subjects: Active vision; Applied sciences; Electric, optical and optoelectronic circuits; Electronics; Exact sciences and technology; Invariant representation; Motion detection; Neural networks; Robot control; Saccade planning; Vector associative maps; Active vision; Vector associative maps; Saccade planning; Motion detection; Invariant representation; Robot control; Learning; Computer vision; Active system; Control system; Invariance; Neural network; Robot
• ISSN: 0893-6080; 1879-2782
• DOI: 10.1016/S0893-6080(97)00076-2

There has been a considerable interest in using active vision for various applications. This interest is primarily because active vision can enhance machine vision capabilities by dynamically changing the camera 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 invariant 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 learn a joint-based representation. Computer simulations and experiments are first performed to evaluate the effectiveness of this scheme using the University of Illinois Active Vision System (UIAVS). The invariance 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 manipulator.