Object Classification using Neuromorphic Cameras

Authors

Presenter(s)

Wes Baldwin

Files

Description

This poster presents recent work in the implementation of dimensionality reduction for neuromorphic camera data using time-surfaces. Neuromorphically inspired cameras can operate at extremely high temporal resolution (>800kHz), low latency (20 microseconds), wide dynamic range (>120dB), and low power (30mW). Time-surfaces are an ideal tool to leverage machine learning on event camera datasets as they assist in noise removal while retaining a high degree of spatial and temporal information. Combining time-surfaces with transfer learning is advancing state-of-the-art performance for object classification.

Publication Date

4-24-2019

Project Designation

Independent Research

Primary Advisor

Vijayan K. Asari

Primary Advisor's Department

Electrical and Computer Engineering

Keywords

Stander Symposium project

Recommended Citation

"Object Classification using Neuromorphic Cameras" (2019). Stander Symposium Projects. 1551.
https://ecommons.udayton.edu/stander_posters/1551