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Shape registration using characteristic functions

Shape registration using characteristic functions
Shape registration using characteristic functions
This paper presents a fast algorithm for the registration of shapes implicitly represented by their characteristic functions. The proposed algorithm aims to recover the transformation parameters (scaling, rotation, and translation) by minimizing a dissimilarity term between two shapes. The algorithm is based on phase correlation and statistical shape moments to compute the registration parameters individually. The algorithm proposed here is applied to various registration problems, to address issues such as the registration of shapes with various topologies, and registration of complex shapes containing various numbers of sub-shapes. Our method proposed here is characterized with a better performance for registration over large databases of shapes, a better accuracy, a higher convergence speed and robustness at the presence of excessive noise in comparison with other state-of-the-art shape registration algorithms in the literature
249-260
Al-Huseiny, Muayed
89bace65-62ba-4531-a4c2-bae3f1dd0c0f
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Al-Huseiny, Muayed
89bace65-62ba-4531-a4c2-bae3f1dd0c0f
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf

Al-Huseiny, Muayed and Mahmoodi, Sasan (2014) Shape registration using characteristic functions. IET Image Processing, 9 (3), 249-260. (doi:10.1049/iet-ipr.2014.0467).

Record type: Article

Abstract

This paper presents a fast algorithm for the registration of shapes implicitly represented by their characteristic functions. The proposed algorithm aims to recover the transformation parameters (scaling, rotation, and translation) by minimizing a dissimilarity term between two shapes. The algorithm is based on phase correlation and statistical shape moments to compute the registration parameters individually. The algorithm proposed here is applied to various registration problems, to address issues such as the registration of shapes with various topologies, and registration of complex shapes containing various numbers of sub-shapes. Our method proposed here is characterized with a better performance for registration over large databases of shapes, a better accuracy, a higher convergence speed and robustness at the presence of excessive noise in comparison with other state-of-the-art shape registration algorithms in the literature

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Published date: 19 September 2014
Organisations: Southampton Wireless Group

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Local EPrints ID: 367651
URI: http://eprints.soton.ac.uk/id/eprint/367651
PURE UUID: 46192288-9f93-48f8-897b-35cd1eecb9aa

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Date deposited: 04 Aug 2014 13:04
Last modified: 07 Oct 2019 18:31

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