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 introduces an event-based luminance-free feature from the output of asynchronous event-based neuromorphic retinas. The feature consists in mapping into a matrix, the distribution of the optical flow along the contours of the moving objects in the visual scene. Asynchronous event-based neuromorphic retinas are composed of autonomous pixels, each of them asynchronously generating ''spiking'' events that encode relative changes in pixels' illumination at high temporal resolutions. The optical flow is computed at each event, and is integrated locally or globally in a speed and direction coordinate frame based grid, using speed-tuned temporal kernels. The latter ensures that the resulting feature represents equitably the distribution of the normal motion along the current moving edges, whatever their respective dynamics. The usefulness and the genericness of the proposed feature are demonstrated in pattern recognition applications: local corner detection and global gesture recognition.