Morphological neural networks for robust visual processing in mobile robotics

Morphological Neural Networks (MNN) have been proposed as associative (with its two cases: autoassociative and heteroassociative) memories. In this paper we are involved with Heteroassociative MNN (HMNN). We propose their utilization as a preprocessing step for human shape detection, in a vision-based navigation problem for mobile robots. MNN can be trained in a single computing step, they possess unlimited storing capacity, and they have perfect recall of the pattens. Recall is also very fast, because the MNN recall does not involve the search for an energy minimum.