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Extended three-dimensional rotation invariant local binary patterns

Extended three-dimensional rotation invariant local binary patterns
Extended three-dimensional rotation invariant local binary patterns
This paper presents a new set of three-dimensional rotation invariant texture descriptors based on the well-known local binary patterns (LBP). In the approach proposed here, we extend an existing three-dimensional LBP based on the region growing algorithm using existing features developed exquisitely for two-dimensional LBPs (pixel intensities and differences). We have conducted experiments on a synthetic dataset of three-dimensional randomly rotated texture images in order to evaluate the discriminatory power and the rotation invariant properties of our descriptors as well as those of other two-dimensional and three-dimensional texture descriptors. Our results demonstrate the effectiveness of the extended LBPs and improvements against other state-of-the-art hand-crafted three-dimensional texture descriptors on this dataset. Furthermore, we prove that the extended LBPs can be used in medical datasets to discriminate between MR images of oxygenated and non-oxygenated brain tissues of newborn babies.
Local binary patterns (LBP), Three-dimensions, Rotation invariance, Texture classification
0262-8856
8-18
Citraro, Leonardo
b94801ff-8d03-47c1-87f7-fab1813a9569
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Darker, Angela
0d0f834d-761e-4bc2-b9f8-49c624de6514
Vollmer, Brigitte
044f8b55-ba36-4fb2-8e7e-756ab77653ba
Citraro, Leonardo
b94801ff-8d03-47c1-87f7-fab1813a9569
Mahmoodi, Sasan
91ca8da4-95dc-4c1e-ac0e-f2c08d6ac7cf
Darker, Angela
0d0f834d-761e-4bc2-b9f8-49c624de6514
Vollmer, Brigitte
044f8b55-ba36-4fb2-8e7e-756ab77653ba

Citraro, Leonardo, Mahmoodi, Sasan, Darker, Angela and Vollmer, Brigitte (2017) Extended three-dimensional rotation invariant local binary patterns. Image and Vision Computing, 62, 8-18. (doi:10.1016/j.imavis.2017.03.004).

Record type: Article

Abstract

This paper presents a new set of three-dimensional rotation invariant texture descriptors based on the well-known local binary patterns (LBP). In the approach proposed here, we extend an existing three-dimensional LBP based on the region growing algorithm using existing features developed exquisitely for two-dimensional LBPs (pixel intensities and differences). We have conducted experiments on a synthetic dataset of three-dimensional randomly rotated texture images in order to evaluate the discriminatory power and the rotation invariant properties of our descriptors as well as those of other two-dimensional and three-dimensional texture descriptors. Our results demonstrate the effectiveness of the extended LBPs and improvements against other state-of-the-art hand-crafted three-dimensional texture descriptors on this dataset. Furthermore, we prove that the extended LBPs can be used in medical datasets to discriminate between MR images of oxygenated and non-oxygenated brain tissues of newborn babies.

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More information

Accepted/In Press date: 22 March 2017
e-pub ahead of print date: 31 March 2017
Published date: 1 June 2017
Keywords: Local binary patterns (LBP), Three-dimensions, Rotation invariance, Texture classification
Organisations: Vision, Learning and Control, Clinical & Experimental Sciences

Identifiers

Local EPrints ID: 407407
URI: https://eprints.soton.ac.uk/id/eprint/407407
ISSN: 0262-8856
PURE UUID: c334023d-427e-476e-9a51-a544816cd903
ORCID for Brigitte Vollmer: ORCID iD orcid.org/0000-0003-4088-5336

Catalogue record

Date deposited: 05 Apr 2017 01:09
Last modified: 03 Dec 2019 06:12

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Contributors

Author: Leonardo Citraro
Author: Sasan Mahmoodi
Author: Angela Darker

University divisions

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