Biologically inspired intensity and depth image edge extraction

Kerr, D., Coleman, S. and McGinnity, T.M. ORCID: 0000-0002-9897-4748, 2018. Biologically inspired intensity and depth image edge extraction. IEEE Transactions on Neural Networks and Learning Systems, PP (99), pp. 1-10. ISSN 2162-237X

[img]
Preview
Text
PubSub10012_McGinnity.pdf - Post-print

Download (6MB) | Preview

Abstract

In recent years artificial vision research has moved from focusing on the use of only intensity images to include using depth images, or RGB-D combinations due to the recent development of low cost depth cameras. However, depth images require a lot of storage and processing requirements. In addition, it is challenging to extract relevant features from depth images in real-time. Researchers have sought inspiration from biology in order to overcome these challenges resulting in biologically inspired feature extraction methods. By taking inspiration from nature it may be possible to reduce redundancy, extract relevant features, and process an image efficiently by emulating biological visual processes. In this paper, we present a depth and intensity image feature extraction approach that has been inspired by biological vision systems. Through the use of biologically inspired spiking neural networks we emulate functional computational aspects of biological visual systems. Results demonstrate that the proposed bio-inspired artificial vision system has increased performance over existing computer vision feature extraction approaches.

Item Type: Journal article
Publication Title: IEEE Transactions on Neural Networks and Learning Systems
Creators: Kerr, D., Coleman, S. and McGinnity, T.M.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 20 February 2018
Volume: PP
Number: 99
ISSN: 2162-237X
Identifiers:
NumberType
10.1109/tnnls.2018.2797994DOI
Divisions: Schools > School of Science and Technology
Depositing User: Linda Sullivan
Date Added: 22 Feb 2018 11:19
Last Modified: 22 Feb 2018 11:24
URI: http://irep.ntu.ac.uk/id/eprint/32785

Actions (login required)

Edit View Edit View

Views

Views per month over past year

Downloads

Downloads per month over past year