Event-Based Visual Flow

被引：270

作者：

Benosman, Ryad ^{[1
,2
]}

Clercq, Charles ^{[3
]}

Lagorce, Xavier ^{[1
]}

Ieng, Sio-Hoi ^{[1
]}

Bartolozzi, Chiara ^{[3
]}

机构：

[1] Univ Paris 06, Inst Vis, F-75252 Paris 05, France

[2] Univ Genoa, DIST, I-16121 Genoa, Italy

[3] Ist Italiano Tecnol, Dept Robot, I-16163 Genoa, Italy

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2014年 / 25卷 / 02期

关键词：

Event-based vision; event-based visual motion flow; neuromorphic sensors; real time; OPTICAL-FLOW; DRIVEN;

D O I：

10.1109/TNNLS.2013.2273537

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper introduces a new methodology to compute dense visual flow using the precise timings of spikes from an asynchronous event-based retina. Biological retinas, and their artificial counterparts, are totally asynchronous and data-driven and rely on a paradigm of light acquisition radically different from most of the currently used frame-grabber technologies. This paper introduces a framework to estimate visual flow from the local properties of events' spatiotemporal space. We will show that precise visual flow orientation and amplitude can be estimated using a local differential approach on the surface defined by coactive events. Experimental results are presented; they show the method adequacy with high data sparseness and temporal resolution of event-based acquisition that allows the computation of motion flow with microsecond accuracy and at very low computational cost.

引用

页码：407 / 417

页数：11

共 34 条

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