title: Asynchronous event-based hebbian epipolar geometry.
abstract: Epipolar geometry, the cornerstone of perspective stereo vision, has been studied extensively since the advent of computer vision. Establishing such a geometric constraint is of primary importance, as it allows the recovery of the 3-D structure of scenes. Estimating the epipolar constraints of nonperspective stereo is difficult, they can no longer be defined because of the complexity of the sensor geometry. This paper will show that these limitations are, to some extent, a consequence of the static image frames commonly used in vision. The conventional frame-based approach suffers from a lack of the dynamics present in natural scenes. We introduce the use of neuromorphic event-based--rather than frame-based--vision sensors for perspective stereo vision. This type of sensor uses the dimension of time as the main conveyor of information. In this paper, we present a model for asynchronous event-based vision, which is then used to derive a general new concept of epipolar geometry linked to the temporal activation of pixels. Practical experiments demonstrate the validity of the approach, solving the problem of estimating the fundamental matrix applied, in a first stage, to classic perspective vision and then to more general cameras. Furthermore, this paper shows that the properties of event-based vision sensors allow the exploration of not-yet-defined geometric relationships, finally, we provide a definition of general epipolar geometry deployable to almost any visual sensor.
authors.affiliation: Vision Institute and the Institute of Intelligent Systems and Robotics, University Pierre and Marie Curie, Paris 75252 Cedex 05, France. ryad.benosman@upmc.fr
authors.forename: Ryad
authors.initials: R
authors.lastname: Benosman
authors.forename: Sio-Hoï
authors.initials: SH
authors.lastname: Ieng
authors.forename: Paul
authors.initials: P
authors.lastname: Rogister
authors.forename: Christoph
authors.initials: C
authors.lastname: Posch
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D040761:Biomimetic Materials; D007090:Image Interpretation, Computer-Assisted; D008027:Light; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D012160:Retina; D012677:Sensation; D013028:Space Perception; D015348:Vision, Binocular