Shapes characterization on event address representation using histograms of oriented events and an extended LBP approach

Abstract

Address Event Representation is a thriving technology that can change the digital image processing paradigm. This paper proposes a methodology to characterize the shape of objects using the streaming of asynchronous events. A new descriptor that enhances spikes connectivity is associated with two oriented histogram based representations. This paper uses those features to develop both a non-supervised and supervised multi-classification framework to recognize poker signs from the Poker-DVS public dataset.