A Sparse Coding Multi-Scale Precise-Timing Machine Learning Algorithm for Neuromorphic Event-Based Sensors

04/24/2018
by   Germain Haessig, et al.
2

This paper introduces an unsupervised compact architecture that can extract features and classify the contents of dynamic scenes from the temporal output of a neuromorphic asynchronous event-based camera. Event-based cameras are clock-less sensors where each pixel asynchronously reports intensity changes encoded in time at the microsecond precision. While this technology is gaining more attention, there is still a lack of methodology and understanding of their temporal properties. This paper introduces an unsupervised time-oriented event-based machine learning algorithm building on the concept of hierarchy of temporal descriptors called time surfaces. In this work we show that the use of sparse coding allows for a very compact yet efficient time-based machine learning that lowers both the computational cost and memory need. We show that we can represent visual scene temporal dynamics with a finite set of elementary time surfaces while providing similar recognition rates as an uncompressed version by storing the most representative time surfaces using clustering techniques. Experiments will illustrate the main optimizations and trade-offs to consider when implementing the method for online continuous vs. offline learning. We report results on the same previously published 36 class character recognition task and a 4 class canonical dynamic card pip task, achieving 100 accuracy on each.

READ FULL TEXT

page 4

page 5

page 6

page 7

research
02/26/2020

Inceptive Event Time-Surfaces for Object Classification Using Neuromorphic Cameras

This paper presents a novel fusion of low-level approaches for dimension...
research
10/27/2022

A Novel Approach for Neuromorphic Vision Data Compression based on Deep Belief Network

A neuromorphic camera is an image sensor that emulates the human eyes ca...
research
01/20/2023

An Asynchronous Intensity Representation for Framed and Event Video Sources

Neuromorphic "event" cameras, designed to mimic the human vision system ...
research
11/27/2018

See before you see: Real-time high speed motion prediction using fast aperture-robust event-driven visual flow

Optical flow is a crucial component of the feature space for early visua...
research
11/19/2018

Event-based Gesture Recognition with Dynamic Background Suppression using Smartphone Computational Capabilities

This paper introduces a framework of gesture recognition operating on th...
research
03/14/2016

Investigation of event-based memory surfaces for high-speed tracking, unsupervised feature extraction and object recognition

In this paper we compare event-based decaying and time based-decaying me...

Please sign up or login with your details

Forgot password? Click here to reset