Abstract
This paper introduces an event-based luminance-free feature from the output of asynchronous event-based neuromorphic retinas. The feature consists in mapping the distribution of the optical flow along the contours of the moving objects in the visual scene into a matrix. 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 equitably represents the distribution of the normal motion along the current moving edges, whatever their respective dynamics. The usefulness and the generality of the proposed feature are demonstrated in pattern recognition applications: local corner detection and global gesture recognition.
Highlights
In computer vision, a feature is a more or less compact representation of visual information that is relevant to solve a task related to a given application
We propose a motion-based feature computed on visual information provided by asynchronous image sensors known as neuromorphic retinas
We propose a classification architecture where the problem is framed as a Bayes filter, that is estimating the probabilities of gestures recursively over time using incoming measurements, given as the hoof-like features ht0:tk ∈ H computed globally from every visual events [e0, ek]
Summary
A feature is a more or less compact representation of visual information that is relevant to solve a task related to a given application (see Laptev, 2005; Mikolajczyk and Schmid, 2005; Mokhtarian and Mohanna, 2006; Moreels and Perona, 2007; Gil et al, 2010; Dickscheid et al, 2011; Gauglitz et al, 2011). We propose a motion-based feature computed on visual information provided by asynchronous image sensors known as neuromorphic retinas (see Delbrück et al, 2010; Posch, 2015). The ATIS (“Asynchronous Time-based Image Sensor,” Posch et al, 2010; Posch, 2015), one of the neuromorphic visual sensors used in this work, is a time-domain encoding image sensor with QVGA resolution. It contains an array of fully autonomous pixels that combine an illuminance change detector circuit, associated to the PD1 photodiode, see Figure 1A and a conditional exposure measurement block, associated to the PD2 photodiode. The exposure measurement circuit encodes the absolute instantaneous pixel illuminance into the timing of asynchronous event pulses, more precisely
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