KYDON VISION SYSTEM: THE ADAPTIVE LEARNING MODEL

Abstract

In this paper, an adaptive learning model for an autonomous vision system multi-layers architecture, called Kydon, are presented, modeled, and analyzed. In particular two critical (deletion and saturation) points on the learning curve are evaluated. These points represent two extreme states on the learning process. The Kydon architecture consists of ‘k’ layers array processors. The lowest layers consists of lower-level processing layers, and the rest consists of higher-level processing layers. The interconnectivity of the PEs in each array is based on a full hexagonal mesh structure. Kydon uses graph models to represent and process the knowledge, extracted from the image. The knowledge base of Kydon is distributed among its PE’s. A unique model for evolving knowledge base has been developed especially for Kydon in order to provide it with some intelligence properties.