Cellular Neural Networks in Active Vision System

In this paper, the application of CNN associative memories for 3D object recognition is presented. The main idea is to analyse the optical flow in an image sequence of an object. Several features of the optical flow between two succeeding images are calculated and merged to a time series of features for the whole image sequence. These features show several object specific characteristics and are used for a classification step in an object recognition system. Therefore, the feature vectors of an object set are learned and recalled by an associative memory. In our experiments we are successfully using an associative memory based on the paradigm of Cellular Neural Networks, CNN. Like Liu and Michel showed, learning in an CNN can be done in a systematic manner by a synthesis procedure which stores all desired memory patterns as reachable memory vectors.