Address Event Representation is a thriving technology that could change 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 these features to develop both a non-supervised and a supervised multi-classification framework to recognize poker symbols from the Poker-DVS public dataset. The aforementioned framework, which uses a very limited number of events and a simple class modeling, yields results that challenge more sophisticated methodologies proposed by the state of the art. A feature family based on context shapes is applied to the more challenging 2015 Poker-DVS dataset with a supervised classifier obtaining an accuracy of 98.5 . The system is also applied to the MNIST-DVS dataset yielding an accuracy of 94.6 and 96.3 on digit recognition, for scales 4 and 8 respectively.
This paper describes novel event-based spatio-temporal features called time-surfaces and how they can be used to create a hierarchical event-based pattern recognition architecture. Unlike existing hierarchical architectures for pattern recognition, the presented model relies on a time oriented approach to extract spatio-temporal features from the asynchronously acquired dynamics of a visual scene. These dynamics are acquired using biologically inspired frameless asynchronous event-driven vision sensors. Similarly to cortical structures, subsequent layers in our hierarchy extract increasingly abstract features using increasingly large spatio-temporal windows. The central concept is to use the rich temporal information provided by events to create contexts in the form of time-surfaces which represent the recent temporal activity within a local spatial neighborhood. We demonstrate that this concept can robustly be used at all stages of an event-based hierarchical model. First layer feature units operate on groups of pixels, while subsequent layer feature units operate on the output of lower level feature units. We report results on a previously published 36 class character recognition task and a four class canonical dynamic card pip task, achieving near 100 percent accuracy on each. We introduce a new seven class moving face recognition task, achieving 79 percent accuracy.