Please use this identifier to cite or link to this item: https://doi.org/10.3389/fnins.2015.00374
Title: Benchmarking neuromorphic vision: Lessons learnt from computer vision
Authors: Tan, C
Lallee, S
Orchard, G 
Keywords: silicon
Article
computer program
computer vision
human
image processing
information processing
machine learning
neuromorphic vision
quality control
sensor
vision
visual system
Issue Date: 2015
Citation: Tan, C, Lallee, S, Orchard, G (2015). Benchmarking neuromorphic vision: Lessons learnt from computer vision. Frontiers in Neuroscience 9 (OCT) : 374. ScholarBank@NUS Repository. https://doi.org/10.3389/fnins.2015.00374
Rights: Attribution 4.0 International
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. © 2015 Tan, Lallee and Orchard.
Source Title: Frontiers in Neuroscience
URI: https://scholarbank.nus.edu.sg/handle/10635/183596
ISSN: 16624548
DOI: 10.3389/fnins.2015.00374
Rights: Attribution 4.0 International
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