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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 cross-correlation 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 cross-correlation 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.

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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

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Date deposited: 15 Jul 2013 09:51
Last modified: 14 Mar 2024 14:20

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Contributors

Author: Wanmu Liu
Author: Sasan Mahmoodi
Author: Tom Havelock
Author: Michael Bennett

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