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Address Event Representation is a thriving technology that could changedigital image processing paradigm. This paper proposes a methodology tocharacterize the shape of objects using the streaming of asynchronous events. Anew descriptor that enhances spikes connectivity is associated with twooriented histogram based representations. This paper uses these features todevelop both a non-supervised and a supervised multi-classification frameworkto recognize poker symbols from the Poker-DVS public dataset. Theaforementioned framework, which uses a very limited number of events and asimple class modeling, yields results that challenge more sophisticatedmethodologies proposed by the state of the art. A feature family based oncontext shapes is applied to the more challenging 2015 Poker-DVS dataset with asupervised classifier obtaining an accuracy of 98.5 %. The system is alsoapplied to the MNIST-DVS dataset yielding an accuracy of 94.6 % and 96.3 % ondigit recognition, for scales 4 and 8 respectively.

Shapes Characterization on Address Event Representation Using Histograms of Oriented Events and an Extended LBP Approach