Vectorization of Cellular Automaton-Based Labeling of 3-D Binary Lattices

Abstract

Labeling connected components in binary lattices is a basic function in image processing with applications in a range of fields, such as robotic vision, machine learning, and even computational fluid dynamics (CFD, percolation theory). While standard algorithms often employ recursive designs that seem ill-suited for parallel execution as well as being prone to excessive memory consumption and even stack-overflows, the described new algorithm is based on a cellular automaton (CA) that is immune against these drawbacks. Furthermore, being an inherently parallel system in itself, the CA also promises speedup and scalability on vector supercomputers as well as on current accelerators, such as GPGPU and Xeon PHI.