Real time texture classification using field programmable gate arrays

Abstract

In this paper we present a novel hardware/software approach to implement a highly accurate texture classification algorithm. We propose the use of field programmable gate arrays (FPGAs) to efficiently compute multiple convolutions in parallel that is required by the spectral histogram representation we employ. The combination of custom hardware and software allows us to have a classifier that is able to achieve results of over 99% accuracy at a rate of roughly 6000 image classifications per second on a challenging real texture dataset.