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Erschienen in:
2014 | OriginalPaper | Buchkapitel
Bio-Inspired Optic Flow from Event-Based Neuromorphic Sensor Input
verfasst von : Stephan Tschechne, Roman Sailer, Heiko Neumann
Erschienen in: Artificial Neural Networks in Pattern Recognition
Verlag: Springer International Publishing
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Computational models of visual processing often use frame-based image acquisition techniques to process a temporally changing stimulus. This approach is unlike biological mechanisms that are spike-based and independent of individual frames. The neuromorphic Dynamic Vision Sensor (DVS) [Lichtsteiner et al., 2008] provides a stream of independent visual events that indicate local illumination changes, resembling spiking neurons at a retinal level. We introduce a new approach for the modelling of cortical mechanisms of motion detection along the dorsal pathway using this type of representation. Our model combines filters with spatio-temporal tunings also found in visual cortex to yield spatio-temporal and direction specificity. We probe our model with recordings of test stimuli, articulated motion and ego-motion. We show how our approach robustly estimates optic flow and also demonstrate how this output can be used for classification purposes.