Image reconstruction from local binary patterns
Image reconstruction from local binary patterns
Reconstruction of an image from its LBP codes can aid understanding of the information contained within the codes by comparing the reconstructed image to the original. We are the first to show that the LBP process can be inverted and present a novel algorithm to perform the reconstruction, resulting in an approximation of the original image that is both visually appealing and completely matches the LBP codes of the original. The algorithm calculates the minimum contrast between two pixels; reconstructing some of the contrast information thought lost in the LBP process. Tests on the algorithm have been conducted on images from the Brodatz database and Berkeley Segmentation Dataset which show an image visually similar to the original with perfect texture construction. The reconstructed images also remove the effects of illumination from the images, suggesting future investigation into the possibility of image brightness normalisation. Additionally, since the reconstructed image provides the same LBP codes as the original, the susceptibility to spoofing of systems using LBP feature vectors has been identified.
118-123
Waller, Ben
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Nixon, Mark S.
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Carter, John N.
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December 2013
Waller, Ben
a802857c-78ee-4290-a400-20d9ef2b0f5e
Nixon, Mark S.
2b5b9804-5a81-462a-82e6-92ee5fa74e12
Carter, John N.
e05be2f9-991d-4476-bb50-ae91606389da
Waller, Ben, Nixon, Mark S. and Carter, John N.
(2013)
Image reconstruction from local binary patterns.
IEEE International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Kyoto, Japan.
02 - 05 Dec 2013.
.
(doi:10.1109/SITIS.2013.30).
Record type:
Conference or Workshop Item
(Paper)
Abstract
Reconstruction of an image from its LBP codes can aid understanding of the information contained within the codes by comparing the reconstructed image to the original. We are the first to show that the LBP process can be inverted and present a novel algorithm to perform the reconstruction, resulting in an approximation of the original image that is both visually appealing and completely matches the LBP codes of the original. The algorithm calculates the minimum contrast between two pixels; reconstructing some of the contrast information thought lost in the LBP process. Tests on the algorithm have been conducted on images from the Brodatz database and Berkeley Segmentation Dataset which show an image visually similar to the original with perfect texture construction. The reconstructed images also remove the effects of illumination from the images, suggesting future investigation into the possibility of image brightness normalisation. Additionally, since the reconstructed image provides the same LBP codes as the original, the susceptibility to spoofing of systems using LBP feature vectors has been identified.
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Published date: December 2013
Venue - Dates:
IEEE International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), Kyoto, Japan, 2013-12-02 - 2013-12-05
Organisations:
Vision, Learning and Control
Identifiers
Local EPrints ID: 363304
URI: http://eprints.soton.ac.uk/id/eprint/363304
PURE UUID: daf6dd85-addf-441b-a204-3321e9eaa5b0
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Date deposited: 20 Mar 2014 16:24
Last modified: 15 Mar 2024 02:35
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Author:
Ben Waller
Author:
John N. Carter
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