Untersuchungen zur Implementierung von Bildverarbeitungsalgorithmen mittels pulsgekoppelter neuronaler Netze

TL;DR

This thesis explores implementing image processing algorithms using pulse-coupled neural networks inspired by biological vision, aiming to replicate highly parallel visual tasks efficiently in hardware.

Contribution

It introduces a method to implement image processing algorithms with pulse-coupled neural nets inspired by biological systems, emphasizing hardware realization of parallel processing.

Findings

Pulse-coupled neural nets can perform image analysis tasks efficiently.

Biological inspiration enables parallel processing with less computational effort.

Potential hardware implementations of these algorithms are feasible.

Abstract

This thesis deals with the study of image processing algorithms which can be implemented by pulse-coupled neural nets. The inspiration for this choice is taken from biological image processing, which achieves with little computational effort in highly parallel processes image analysis tasks such as object recognition, image segmentation, velocity and distance estimation, etc. Conventional, serially implemented algorithms either cannot realize those tasks at all or will expend significantly more effort. Because the first stages of the visual system comprise a sensor interface, they are comparatively accessible with respect to defining their transfer or processing function. Some of those processing functions or principles are to be used in hardware implementations, with the focus on duplicating especially the highly parallel processing.