Memristive fuzzy edge detector

TL;DR

This paper introduces a memristive fuzzy edge detector that uses a neuro-fuzzy system based on memristor crossbars to perform real-time edge detection in grayscale images, overcoming hardware implementation challenges.

Contribution

It proposes a novel memristive neuro-fuzzy architecture with fuzzy minterms for efficient hardware implementation of fuzzy inference systems.

Findings

Real-time edge detection of grayscale images.

Comparison shows advantages over traditional edge detectors.

Analog implementation enables simultaneous edge extraction.

Abstract

Fuzzy inference systems always suffer from the lack of efficient structures or platforms for their hardware implementation. In this paper, we tried to overcome this problem by proposing new method for the implementation of those fuzzy inference systems which use fuzzy rule base to make inference. To achieve this goal, we have designed a multi-layer neuro-fuzzy computing system based on the memristor crossbar structure by introducing some new concepts like fuzzy minterms. Although many applications can be realized through the use of our proposed system, in this study we show how the fuzzy XOR function can be constructed and how it can be used to extract edges from grayscale images. Our memristive fuzzy edge detector (implemented in analog form) compared with other common edge detectors has this advantage that it can extract edges of any given image all at once in real-time.