Implementation of Neuro-Fuzzy System based image edge detection

In this paper, we propose an implementation of a Neuro-Fuzzy System (NFS) with on chip learning for achieving different image processing tasks such as filtering, edge detection, etc. The complexity of this kind of implementation makes the pulse mode an important approach to achieve our goal thanks to its higher density of integration. As validation example, we propose here the edge detection process to be approximated by this system. The proposed system has proven a good approximation ability with a reduced neuron number and learning time cost. Moreover, the efficiency of our proposed system versus conventional edge detection operators is demonstrated. For different error criteria, our design shows the lowest values. The designed system is implemented on a field-programmable gate array (FPGA) platform. Synthesis results prove that the implemented NFS provides the best compromise between compactness, speed and accuracy compared to previous works from literature.