The University of Southampton
University of Southampton Institutional Repository

On extending the capability of the image ray transform

On extending the capability of the image ray transform
On extending the capability of the image ray transform
A conventional approach to image analysis is to separately perform feature extraction at a low level (such as edge detection) and follow this with high level feature extraction to determine structure (e.g. by collecting edge points) using the Hough transform. The original Image Ray Transform (IRT) demonstrated capability to emphasise structures at a low level. Here we extend the IRT to add shape specificity that makes it select specific shapes rather than just edges; the new capability is achieved by addition of a single parameter that controls which shape is selected by the extended IRT. The extended approach can then perform low-and high-level feature extraction simultaneously. We show how the IRT process can be extended to focus on chosen shapes such as lines and circles. We also suggest an additional extension of IRT to detect shapes of chosen colours. The new approach uses the CIEL*a*b* colour model within the IRT’s light ray analogy. The capability of the extended IRT using colour information is evaluated for correct shape location by conventional methods such as the Hough Transform. We analyse performance with images from the Caltech-256 dataset and show that the new approach can indeed select chosen shapes and colours. We also show how the new approach has the capability to detect objects with specific shape and colour. Further research will aim to capitalise on the new extraction ability to extend descriptive capability.
Oh, Ah Reum
914c4cc3-57da-492c-8a87-f1300a5663cf
Oh, Ah Reum
914c4cc3-57da-492c-8a87-f1300a5663cf
Nixon, Mark
2b5b9804-5a81-462a-82e6-92ee5fa74e12

(2016) On extending the capability of the image ray transform. University of Southampton, Faculty of Physical Sciences and Engineering, Doctoral Thesis, 141pp.

Record type: Thesis (Doctoral)

Abstract

A conventional approach to image analysis is to separately perform feature extraction at a low level (such as edge detection) and follow this with high level feature extraction to determine structure (e.g. by collecting edge points) using the Hough transform. The original Image Ray Transform (IRT) demonstrated capability to emphasise structures at a low level. Here we extend the IRT to add shape specificity that makes it select specific shapes rather than just edges; the new capability is achieved by addition of a single parameter that controls which shape is selected by the extended IRT. The extended approach can then perform low-and high-level feature extraction simultaneously. We show how the IRT process can be extended to focus on chosen shapes such as lines and circles. We also suggest an additional extension of IRT to detect shapes of chosen colours. The new approach uses the CIEL*a*b* colour model within the IRT’s light ray analogy. The capability of the extended IRT using colour information is evaluated for correct shape location by conventional methods such as the Hough Transform. We analyse performance with images from the Caltech-256 dataset and show that the new approach can indeed select chosen shapes and colours. We also show how the new approach has the capability to detect objects with specific shape and colour. Further research will aim to capitalise on the new extraction ability to extend descriptive capability.

PDF
Final_Thesis_AhReum_OH.pdf - Other
Available under License University of Southampton Thesis Licence.
Download (8MB)

More information

Published date: June 2016
Organisations: University of Southampton, Vision, Learning and Control

Identifiers

Local EPrints ID: 400262
URI: http://eprints.soton.ac.uk/id/eprint/400262
PURE UUID: 827b7479-37ec-4567-b6f9-393ac746f705
ORCID for Mark Nixon: ORCID iD orcid.org/0000-0002-9174-5934

Catalogue record

Date deposited: 26 Sep 2016 15:25
Last modified: 06 Jun 2018 13:18

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.

×