Presenter(s)

    Wes Baldwin

    Download

    Download Project (2.6 MB)

    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

    Object Classification using Neuromorphic Cameras

    Share

    COinS