dc.contributor.author |
Mgaga, Sboniso S
|
|
dc.contributor.author |
Tapamo, JR
|
|
dc.contributor.editor |
Bebis, G |
|
dc.date.accessioned |
2021-02-18T09:04:01Z |
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dc.date.available |
2021-02-18T09:04:01Z |
|
dc.date.issued |
2020-12 |
|
dc.identifier.citation |
Mgaga, S.S. & Tapamo, J. 2020. Optical coherence tomography latent fingerprint image denoising. In <i>15th International Symposium on Visual Computing, San Diego, CA, USA, 5-7 October 2020. Published in: Bebis G. et al. (eds) Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science, vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_55</i>. G. Bebis, Ed. S.l.: Springer. http://hdl.handle.net/10204/11792 . |
en_ZA |
dc.identifier.isbn |
978-3-030-64555-7 |
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dc.identifier.uri |
http://hdl.handle.net/10204/11792
|
|
dc.description.abstract |
Latent fingerprints are fingerprint impressions left on the surfaces a finger comes into contact with. They are found in almost every crime scene. Conventionally, latent fingerprints have been obtained using chemicals or physical methods, thus destructive techniques. Forensic community is moving towards contact-less acquisition methods. The contact-less acquisition presents some advantages over destructive methods; such advantages include multiple acquisitions of the sample and a possibility of further analysis such as touch DNA. This work proposes a speckle-noise denoising method for optical coherence tomography (OCT) latent fingerprint images. The proposed denoising technique was derived from the adaptive threshold and the normal shrinkage. Experimental results have shown that the proposed method suppressed specklenoise better than the adaptive threshold, NormalShrink, VisuShrink, SUREShrink and BayesShrink. |
en_US |
dc.format |
Abstract |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
Springer |
en_US |
dc.relation.uri |
https://link.springer.com/book/10.1007/978-3-030-64556-4 |
en_US |
dc.relation.uri |
https://doi.org/10.1007/978-3-030-64559-5_55 |
en_US |
dc.source |
15th International Symposium on Visual Computing, San Diego, CA, USA, 5-7 October 2020. Published in: Bebis G. et al. (eds) Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science, vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_55 |
en_US |
dc.subject |
Denoising |
en_US |
dc.subject |
Latent fingerprints |
en_US |
dc.subject |
Wavelet thresholding |
en_US |
dc.subject |
Optical coherence tomography |
en_US |
dc.subject |
Biometrics |
en_US |
dc.title |
Optical coherence tomography latent fingerprint image denoising |
en_US |
dc.type |
Book Chapter |
en_US |
dc.description.pages |
694-705 |
en_US |
dc.description.placeofpublication |
Cham, Switzerland |
en_US |
dc.description.note |
Copyright: 2020 Springer Nature. This is the abstract version of the work. For access to the fulltext, please visit the publisher's website. |
en_US |
dc.description.cluster |
Defence and Security |
|
dc.description.impactarea |
Information & Cyber Security C |
|
dc.identifier.apacitation |
Mgaga, S. S., & Tapamo, J. (2020). Optical coherence tomography latent fingerprint image denoising. In G. Bebis. (Ed.), <i>15th International Symposium on Visual Computing, San Diego, CA, USA, 5-7 October 2020. Published in: Bebis G. et al. (eds) Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science, vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_55</i> Springer. http://hdl.handle.net/10204/11792 |
en_ZA |
dc.identifier.chicagocitation |
Mgaga, Sboniso S, and JR Tapamo. "Optical coherence tomography latent fingerprint image denoising" In <i>15TH INTERNATIONAL SYMPOSIUM ON VISUAL COMPUTING, SAN DIEGO, CA, USA, 5-7 OCTOBER 2020. PUBLISHED IN: BEBIS G. ET AL. (EDS) ADVANCES IN VISUAL COMPUTING. ISVC 2020. LECTURE NOTES IN COMPUTER SCIENCE, VOL 12510. SPRINGER, CHAM. HTTPS://DOI.ORG/10.1007/978-3-030-64559-5_55</i>, edited by G Bebis. n.p.: Springer. 2020. http://hdl.handle.net/10204/11792. |
en_ZA |
dc.identifier.vancouvercitation |
Mgaga SS, Tapamo J. Optical coherence tomography latent fingerprint image denoising. In Bebis G, editor.. 15th International Symposium on Visual Computing, San Diego, CA, USA, 5-7 October 2020. Published in: Bebis G. et al. (eds) Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science, vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_55. [place unknown]: Springer; 2020. [cited yyyy month dd]. http://hdl.handle.net/10204/11792. |
en_ZA |
dc.identifier.ris |
TY - Book Chapter
AU - Mgaga, Sboniso S
AU - Tapamo, JR
AB - Latent fingerprints are fingerprint impressions left on the surfaces a finger comes into contact with. They are found in almost every crime scene. Conventionally, latent fingerprints have been obtained using chemicals or physical methods, thus destructive techniques. Forensic community is moving towards contact-less acquisition methods. The contact-less acquisition presents some advantages over destructive methods; such advantages include multiple acquisitions of the sample and a possibility of further analysis such as touch DNA. This work proposes a speckle-noise denoising method for optical coherence tomography (OCT) latent fingerprint images. The proposed denoising technique was derived from the adaptive threshold and the normal shrinkage. Experimental results have shown that the proposed method suppressed specklenoise better than the adaptive threshold, NormalShrink, VisuShrink, SUREShrink and BayesShrink.
DA - 2020-12
DB - ResearchSpace
DP - CSIR
ED - Bebis, G
J1 - 15th International Symposium on Visual Computing, San Diego, CA, USA, 5-7 October 2020. Published in: Bebis G. et al. (eds) Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science, vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_55
KW - Denoising
KW - Latent fingerprints
KW - Wavelet thresholding
KW - Optical coherence tomography
KW - Biometrics
LK - https://researchspace.csir.co.za
PY - 2020
SM - 978-3-030-64555-7
T1 - Optical coherence tomography latent fingerprint image denoising
TI - Optical coherence tomography latent fingerprint image denoising
UR - http://hdl.handle.net/10204/11792
ER - |
en_ZA |
dc.identifier.worklist |
24090 |
en_US |