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Poker-DVS and MNIST-DVS. Their History How They Were Made and Other Details

机译:Poker-DVS和MNIST-DVS。他们的历史制作方式以及其他详细信息

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摘要

This article reports on two databases for event-driven object recognition using a Dynamic Vision Sensor (DVS). The first, which we call Poker-DVS and is being released together with this article, was obtained by browsing specially made poker card decks in front of a DVS camera for 2–4 s. Each card appeared on the screen for about 20–30 ms. The poker pips were tracked and isolated off-line to constitute the 131-recording Poker-DVS database. The second database, which we call MNIST-DVS and which was released in December 2013, consists of a set of 30,000 DVS camera recordings obtained by displaying 10,000 moving symbols from the standard MNIST 70,000-picture database on an LCD monitor for about 2–3 s each. Each of the 10,000 symbols was displayed at three different scales, so that event-driven object recognition algorithms could easily be tested for different object sizes. This article tells the story behind both databases, covering, among other aspects, details of how they work and the reasons for their creation. We provide not only the databases with corresponding scripts, but also the scripts and data used to generate the figures shown in this article (as Supplementary Material).
机译:本文报告了两个使用动态视觉传感器(DVS)进行事件驱动的对象识别的数据库。第一个,我们称为Poker-DVS,将与本文一起发布,是通过在DVS摄像机前面浏览特制的扑克牌组2到4 s而获得的。每张卡在屏幕上出现大约20–30毫秒。跟踪扑克点并脱机隔离,以构成131条记录的Poker-DVS数据库。第二个数据库,我们称为MNIST-DVS,于2013年12月发布,包含一组30,000个DVS摄像机记录,这些记录通过在LCD监视器上显示标准MNIST 70,000张图片数据库中的10,000个运动符号显示大约2-3个而获得。每个。 10,000个符号中的每一个都以三种不同的比例显示,因此可以轻松地针对不同的对象大小测试事件驱动的对象识别算法。本文讲述了这两个数据库背后的故事,除其他方面外,还涵盖了它们如何工作以及创建原因的详细信息。我们不仅为数据库提供了相应的脚本,而且还为生成本文中显示的图(作为补充材料)提供了脚本和数据。

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