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Benchmarking neuromorphic vision: lessons learnt from computer vision.

Cheston Tan¹, Stephane Lallee¹, Garrick Orchard²

¹Agency for Science, Technology, and Research (ASTAR), Institute for Infocomm Research Singapore, Singapore.

Frontiers in Neuroscience

|November 4, 2015

Summary

This summary is machine-generated.

Neuromorphic Vision sensors are advancing, but lack datasets and mature algorithms. This paper reviews frame-based computer vision benchmarks to guide the development of new neuromorphic vision challenges.

Keywords:

benchmarking computer vision datasets neuromorphic vision sensory processing

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Area of Science:

Computer Vision
Neuromorphic Engineering

Background:

Neuromorphic Vision sensors have matured significantly, becoming commercially available.
Despite sensor advancements, there's a critical lack of datasets and nascent algorithms for spike-based data processing.
Frame-based computer vision benefits from mature algorithms and widely accepted datasets, fostering competition and advancement.

Purpose of the Study:

To leverage lessons from frame-based computer vision datasets and benchmarks.
To propose guidelines for creating effective Neuromorphic Vision benchmarks and challenges.
To address the unique challenges in benchmarking Neuromorphic Vision algorithms.

Main Methods:

Reviewing the historical role of benchmarks and challenges in frame-based computer vision.
Analyzing the current state of Neuromorphic Vision datasets and algorithms.
Identifying key considerations for developing new Neuromorphic Vision benchmarks.

Main Results:

Established the importance of datasets and benchmarks in driving progress in computer vision.
Highlighted the disparity between mature frame-based vision and developing neuromorphic vision.
Identified specific challenges in comparing neuromorphic and frame-based vision systems.

Conclusions:

There is a crucial need for well-designed benchmarks and datasets to advance Neuromorphic Vision.
Guidelines are proposed to foster the development of competitive and comparable neuromorphic vision challenges.
Overcoming benchmarking challenges is key to unlocking the full potential of neuromorphic vision technology.