Introducing visual latencies into spin-lattice models for image segmentation: a neuromorphic approach to a computer vision problem

Abstract

In this study we show how an algorithmic principle which might play a role in information processing in the brain of higher vertebrates - the so called visual latencies - can be transferred with high efficiency to a model system which is better suited for implementation on conventional computer hardware. To this end we assign luminance dependent temporal delays (latencies) to the individual pixels of an image. This temporal structure of the input data stream then accelerates and improves the relaxation of a spin-lattice labeling algorithm for scene segmentation.