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Optical coherence tomography latent fingerprint image denoising

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dc.contributor.author Mgaga, Sboniso S
dc.contributor.author Tapamo, JR
dc.contributor.editor Bebis, G
dc.date.accessioned 2021-02-18T09:04:01Z
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
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


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