The University of Southampton
University of Southampton Institutional Repository

On Using Physical Analogies for Feature and Shape Extraction in Computer Vision

On Using Physical Analogies for Feature and Shape Extraction in Computer Vision
On Using Physical Analogies for Feature and Shape Extraction in Computer Vision
There is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low- and for high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but with performance analysis and optimization impeded by speed of computation. We have developed new feature extraction techniques on notional use of physical paradigms, with parametrization aimed to be more familiar to a scientifically trained user, aiming to make best use of computational resource. This paper is the first unified description of these new approaches, outlining the basis and results that can be achieved. We describe how gravitational force can be used for low-level analysis, while analogies of water flow and heat can be deployed to achieve high-level smooth shape detection, by determining features and shapes in a selection of images, comparing results with those by stock approaches from the literature. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision.
11-25
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Hurley, David
d0abd3e5-ffac-4160-bb00-042083251d79
Liu, Xin
82424b16-94ac-4de7-972f-b384d98cba3f
Direkoglu, Cem
b793e59b-4188-44b2-99c5-b4dedc46cfda
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Hurley, David
d0abd3e5-ffac-4160-bb00-042083251d79
Liu, Xin
82424b16-94ac-4de7-972f-b384d98cba3f
Direkoglu, Cem
b793e59b-4188-44b2-99c5-b4dedc46cfda

Nixon, Mark, Hurley, David, Liu, Xin and Direkoglu, Cem (2011) On Using Physical Analogies for Feature and Shape Extraction in Computer Vision. The Computer Journal, 54 (1), 11-25.

Record type: Article

Abstract

There is a rich literature of approaches to image feature extraction in computer vision. Many sophisticated approaches exist for low- and for high-level feature extraction but can be complex to implement with parameter choice guided by experimentation, but with performance analysis and optimization impeded by speed of computation. We have developed new feature extraction techniques on notional use of physical paradigms, with parametrization aimed to be more familiar to a scientifically trained user, aiming to make best use of computational resource. This paper is the first unified description of these new approaches, outlining the basis and results that can be achieved. We describe how gravitational force can be used for low-level analysis, while analogies of water flow and heat can be deployed to achieve high-level smooth shape detection, by determining features and shapes in a selection of images, comparing results with those by stock approaches from the literature. We also aim to show that the implementation is consistent with the original motivations for these techniques and so contend that the exploration of physical paradigms offers a promising new avenue for new approaches to feature extraction in computer vision.

Text
nixon_computer_journal.pdf - Version of Record
Download (1MB)

More information

Published date: January 2011
Organisations: Vision, Learning and Control

Identifiers

Local EPrints ID: 267770
URI: http://eprints.soton.ac.uk/id/eprint/267770
PURE UUID: 93934e14-5719-4af6-95a1-a8d15d7af182
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 11 Aug 2009 11:30
Last modified: 26 Nov 2019 02:08

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×