The University of Southampton
University of Southampton Institutional Repository

Robust similarity registration technique for volumetric shapes represented by characteristic functions

Robust similarity registration technique for volumetric shapes represented by characteristic functions
Robust similarity registration technique for volumetric shapes represented by characteristic functions
This paper proposes a novel similarity registration technique for volumetric shapes implicitly represented by their characteristic functions (CFs). Here, the calculation of rotation parameters is considered as a spherical crosscorrelation problem and the solution is therefore found using the standard phase correlation technique facilitated by principal components analysis (PCA).
Thus, fast Fourier transform (FFT) is employed to vastly improve efficiency and robustness. Geometric moments are then used for shape scale estimation which is independent from rotation and translation parameters. It is numerically
demonstrated that our registration method is able to handle shapes with various topologies and robust to noise and initial poses. Further validation of our method is performed by registering a lung database.
0031-3203
1144-1158
Liu, Wanmu
9e0d3ce3-bc41-4170-940c-0b468cb13d45
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Havelock, Tom
8f9aa3be-a768-4a7b-bcd4-79656e2b188c
Bennett, Michael
6df5585a-3d93-4870-8797-389759fc82c7
Liu, Wanmu
9e0d3ce3-bc41-4170-940c-0b468cb13d45
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Havelock, Tom
8f9aa3be-a768-4a7b-bcd4-79656e2b188c
Bennett, Michael
6df5585a-3d93-4870-8797-389759fc82c7

Liu, Wanmu, Mahmoodi, Sasan, Havelock, Tom and Bennett, Michael (2014) Robust similarity registration technique for volumetric shapes represented by characteristic functions. Pattern Recognition, 47 (3), 1144-1158. (doi:10.1016/j.patcog.2013.08.013).

Record type: Article

Abstract

This paper proposes a novel similarity registration technique for volumetric shapes implicitly represented by their characteristic functions (CFs). Here, the calculation of rotation parameters is considered as a spherical crosscorrelation problem and the solution is therefore found using the standard phase correlation technique facilitated by principal components analysis (PCA).
Thus, fast Fourier transform (FFT) is employed to vastly improve efficiency and robustness. Geometric moments are then used for shape scale estimation which is independent from rotation and translation parameters. It is numerically
demonstrated that our registration method is able to handle shapes with various topologies and robust to noise and initial poses. Further validation of our method is performed by registering a lung database.

Text
1-s2.0-S0031320313003348-main.pdf - Other
Restricted to Repository staff only
Request a copy

More information

Accepted/In Press date: 13 August 2013
e-pub ahead of print date: 8 October 2013
Published date: November 2014
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 354551
URI: http://eprints.soton.ac.uk/id/eprint/354551
ISSN: 0031-3203
PURE UUID: f57a5335-195f-412f-b6a8-aa038cc32c2b

Catalogue record

Date deposited: 15 Jul 2013 09:51
Last modified: 07 Oct 2019 18:33

Export record

Altmetrics

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.

×