Benchmarking neuromorphic vision : lessons learnt from computer vision

Benchmarking neuromorphic vision : lessons learnt from computer vision
Title:
Benchmarking neuromorphic vision : lessons learnt from computer vision
Other Titles:
Frontiers in Neuroscience
Keywords:
Publication Date:
13 October 2015
Citation:
Tan C, Lallee S and Orchard G (2015) Benchmarking neuromorphic vision: lessons learnt from computer vision. Front. Neurosci. 9:374. doi: 10.3389/fnins.2015.00374
Abstract:
Neuromorphic Vision sensors have improved greatly since the first silicon retina was presented almost three decades ago. They have recently matured to the point where they are commercially available and can be operated by laymen. However, despite improved availability of sensors, there remains a lack of good datasets, while algorithms for processing spike-based visual data are still in their infancy. On the other hand, frame-based computer vision algorithms are far more mature, thanks in part to widely accepted datasets which allow direct comparison between algorithms and encourage competition. We are presented with a unique opportunity to shape the development of Neuromorphic Vision benchmarks and challenges by leveraging what has been learnt from the use of datasets in frame-based computer vision. Taking advantage of this opportunity, in this paper we review the role that benchmarks and challenges have played in the advancement of frame-based computer vision, and suggest guidelines for the creation of Neuromorphic Vision benchmarks and challenges. We also discuss the unique challenges faced when benchmarking Neuromorphic Vision algorithms, particularly when attempting to provide direct comparison with frame-based computer vision.
License type:
http://creativecommons.org/licenses/by/4.0/
Funding Info:
Description:
ISSN:
1662-4548
1662-453X
Files uploaded:

File Size Format Action
tan-et-al-published-pdf.pdf 1.43 MB PDF Open