Asynchronous Event-based Cooperative Stereo Matching Using Neuromorphic Silicon Retinas
Asynchronous Event-based Cooperative Stereo Matching Using Neuromorphic Silicon Retinas
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DOI：
10.1007/s11063-015-9434-5
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发表时间：
2016-04-01
影响因子：
3.1
通讯作者：
Conradt, Joerg
中科院分区：
文献类型：
--
作者：
Firouzi, Mohsen;Conradt, Joerg
生物启发的事件驱动硅视网膜，所谓的动态视觉传感器（DVS），允许各种视觉感知任务的有效解决方案，例如监视，跟踪或运动检测。类似于视网膜光感受器，DVS中的任何感知到的光强度变化都会在相应的像素处生成事件。DVS由此发出时空事件流以编码视觉感知的对象，与传统的基于帧的相机相比，其在很大程度上没有冗余的背景信息。DVS提供了多个额外的优点，但需要开发全新的异步、基于事件的信息处理算法。在本文中，我们提出了一个完全基于事件的视差匹配算法，可靠的三维深度感知使用动态合作神经网络。协作单元之间的交互应用交叉视差唯一性约束和视差内连续性约束，以异步地提取每个新事件的视差，而不需要缓冲各个事件。我们已经研究了该算法的性能在几个实验中，我们的结果表明，平滑的视差图计算在一个纯粹的基于事件的方式，即使在场景与时间重叠的刺激。
Biologically-inspired event-driven silicon retinas, so called dynamic vision sensors (DVS), allow efficient solutions for various visual perception tasks, e.g. surveillance, tracking, or motion detection. Similar to retinal photoreceptors, any perceived light intensity change in the DVS generates an event at the corresponding pixel. The DVS thereby emits a stream of spatiotemporal events to encode visually perceived objects that in contrast to conventional frame-based cameras, is largely free of redundant background information. The DVS offers multiple additional advantages, but requires the development of radically new asynchronous, event-based information processing algorithms. In this paper we present a fully event-based disparity matching algorithm for reliable 3D depth perception using a dynamic cooperative neural network. The interaction between cooperative cells applies cross-disparity uniqueness-constraints and within-disparity continuity-constraints, to asynchronously extract disparity for each new event, without any need of buffering individual events. We have investigated the algorithm's performance in several experiments; our results demonstrate smooth disparity maps computed in a purely event-based manner, even in the scenes with temporally-overlapping stimuli.