Transputer-based architectures for associative image classification

A parallel architecture to support an associative system for image classification is presented. The recognition principle exploits the noise-insensitivity and content-addressability of associative memories to achieve robust classification in image processing; in addition, the implementation on a transputer-based architecture makes it possible at attain high structural flexibility, at relatively low machinery cost. After defining the basic associative-classification mechanism, the parallel structure is described. The resulting system proves quite fast and inexpensive, hence applications to real-time environments become feasible. Structural flexibility allows easy modifications to the system to tailor it to different application domains. The efficiency of the proposed architecture is evidenced by experimental results obtained in a real image-classification domain