Autonomic Computer Vision Systems

Abstract

Most computer vision systems perform well under controlled laboratory conditions, but require lengthy set up and "tuning" by experts when installed in new operating conditions. Unfortunately, for most real applications of computer vision, the operating conditions frequently change. These changes degrade system performance and can even cause complete system failure, requiring intervention by a trained engineer. The requirement for installation and frequent maintenance by highly trained experts seriously inhibits the commercial application of computer vision systems. In this talk we discuss ways in which autonomic computing can reduce the cost of installation and configuration, as well as enhance reliability, for practical computer vision systems. We begin by reviewing the origins of autonomic computing. We then describe the design of a computer vision system as a software component within a layered service architecture. We describe techniques for regulation of internal parameters, error detection and recovery, self description, and self configuration for vision systems. These methods will be illustrated with results from the IST projects FAME, CAVIAR and CHIL.