Adaptive kernels for morphological heteroassociative neural networks

Abstract

Morphological Neural Networks (MNN) have been proposed as an alternative neural computation paradigm. In this paper we explore the potential of Heteroassociative MNN (HMNN) for a vision based practical task, that of self-localization in a vision-based navigation framework for mobile robots. HMNN have a big potential for real time application because its recall process is very fast. We present some experimental results that illustrate the proposed approach.