Journal Code Article ID Dispatch: 15-FEB-19 CE: D, Subramani WIDM 1310 No. of Pages: 34 ME: Received: 6 September 2018 Revised: 23 January 2019 Accepted: 24 January 2019 DOI: 10.1002/widm.1310 1 2 3 ADVANCED REVIEW 4 5 6 Neuromorphic vision: Sensors to event-based algorithms Q2 7 8 1,2 3 2 9 A. Lakshmi | Anirban Chakraborty | Chetan S. Thakur Q1 Q3 10 11 1Centre for Artificial Intelligence and Robotics, Regardless of the marvels brought by the conventional frame-based cameras, they 12 Defence Research and Development Organization, Bangalore, India have significant drawbacks due to their redundancy in data and temporal latency. 13 2Department of Electronic Systems Engineering, This causes problem in applications where low-latency transmission and high- 14 Indian Institute of Science, Bangalore, India speed processing are mandatory. Proceeding on this line of thought, the neurobio- 15 3 Department of Computational and Data Sciences, logical principles of the biological retina have been adapted to accomplish data 16 Indian Institute of Science, Bangalore, India Q4 sparsity and high dynamic range at the pixel level. These bio-inspired neuro- 17 Correspondence morphic vision sensors alleviate the more serious bottleneck of data redundancy by 18 Chetan Singh Thakur, Department of Electronic Systems Engineering, Indian Institute of Science, responding to changes in illumination rather than to illumination itself. This paper 19 Bangalore 560012, India. reviews in brief one such representative of neuromorphic sensors, the activity- 20 Email:
[email protected] driven event-based vision sensor, which mimics human eyes. Spatio-temporal 21 encoding of event data permits incorporation of time correlation in addition to spa- 22 tial correlation in vision processing, which enables more robustness.