Synthesis of a Cellular Nonlinear Network for a Fuzzy Associative Memory in Stereoscopic Vision

In this paper a Cellular Fuzzy Associative Memory containing fuzzy rules for gray image fuzzification is designed, considered as a subsystem of a CNN-based architecture able to store bidimensional patterns. After establishing the fuzzy rules which characterize the required FAM, these rules are properly codified and stored in a Cellular Nonlinear Network behaving as a memory. A numerical example concerning with the stereoscopic vision of a mobile robot is reported to show how the synthesized memory can process bidimensional patterns for robotic vision purposes.