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 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.
Sharma
Rajeev
R
Department of Computer Science and Engineering, The Pennsylvania State University, 317 Pond Laboratory, University Park, PA 16802, USA
Srinivasa
Narayan
N