An integrated associative structure for vision

Abstract

An associative architecture for mapping input images into a set of predefined bit patterns (messages) is described. The running general methodology exploits memory content-addressability to perform robust vision tasks. A noiselike coding associative memory works out message samples from input images, while a superimposed feedforward network filters out memory crosstalk and provides clean messages patterns. The integrated structure combines the generalization power of neural networks with the massive processing capability of associative memories. Tests have involved image sets which stress the system's discrimination efficacy. Experimental results confirmed the system's robustness and flexibility. The overall structure can be regarded as a general domain-independent method for visual stimulus-response mapping.<>