Skin color correction via convolutional neural networks in 3D fringe projection profilometry
Fringe Projection Profilometry (FPP) with Digital Light Projector technology is one of the most reliable 3D sensing techniques for biomedical applications. However, besides the fringe pattern images,often a color texture image is needed for an accurate medical documentation. This image may be acquir...
- Autores:
-
Barrios, Erik
Pineda, Jesus
Romero, Lenny A
Millán, María S
Marrugo, Andrés G.
- Tipo de recurso:
- Fecha de publicación:
- 2021
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12114
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12114
- Palabra clave:
- Color constancy
Convolutional neural network
Image color processing
Machine learning
Skin color correction
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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Skin color correction via convolutional neural networks in 3D fringe projection profilometry |
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Skin color correction via convolutional neural networks in 3D fringe projection profilometry |
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Skin color correction via convolutional neural networks in 3D fringe projection profilometry Color constancy Convolutional neural network Image color processing Machine learning Skin color correction |
| title_short |
Skin color correction via convolutional neural networks in 3D fringe projection profilometry |
| title_full |
Skin color correction via convolutional neural networks in 3D fringe projection profilometry |
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Skin color correction via convolutional neural networks in 3D fringe projection profilometry |
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Skin color correction via convolutional neural networks in 3D fringe projection profilometry |
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Skin color correction via convolutional neural networks in 3D fringe projection profilometry |
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Barrios, Erik Pineda, Jesus Romero, Lenny A Millán, María S Marrugo, Andrés G. |
| dc.contributor.author.none.fl_str_mv |
Barrios, Erik Pineda, Jesus Romero, Lenny A Millán, María S Marrugo, Andrés G. |
| dc.subject.keywords.spa.fl_str_mv |
Color constancy Convolutional neural network Image color processing Machine learning Skin color correction |
| topic |
Color constancy Convolutional neural network Image color processing Machine learning Skin color correction |
| description |
Fringe Projection Profilometry (FPP) with Digital Light Projector technology is one of the most reliable 3D sensing techniques for biomedical applications. However, besides the fringe pattern images,often a color texture image is needed for an accurate medical documentation. This image may be acquired either by projecting a white image or a black image and relying on ambient light. Color Constancy is essential for a faithful digital record, although the optical properties of biological tissue make color reproducibility challenging. Furthermore, color perception is highly dependent on the illuminant. Here, we describe a deep learning-based method for skin color correction in FPP. We trained a convolutional neural network using a skin tone color palette acquired under different illumination conditions to learn the mapping relationship between the input color image and its counterpart in the sRGB color space. Preliminary experimental results demonstrate the potential for this approach. |
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2021 |
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2021-09-02 |
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2023-07-18T19:17:34Z |
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2023-07-18T19:17:34Z |
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2023-07 |
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Barrios, E., Pineda, J., Romero, L.A., Millán, M.S., Marrugo, A.G. Skin color correction via convolutional neural networks in 3D fringe projection profilometry (2021) Proceedings of SPIE - The International Society for Optical Engineering, 11804, art. no. 118041P, . DOI: 10.1117/12.2594331 |
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https://hdl.handle.net/20.500.12585/12114 |
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10.1117/12.2594331 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
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Barrios, E., Pineda, J., Romero, L.A., Millán, M.S., Marrugo, A.G. Skin color correction via convolutional neural networks in 3D fringe projection profilometry (2021) Proceedings of SPIE - The International Society for Optical Engineering, 11804, art. no. 118041P, . DOI: 10.1117/12.2594331 10.1117/12.2594331 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12114 |
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eng |
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eng |
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Barrios, Erikbb277699-e10e-4f85-982c-9a3b98acb515Pineda, Jesusa6827c4e-c14f-4dc1-ba8e-4c5b1cd055ebRomero, Lenny A4e34aa8a-f981-4e1d-ae32-d45acb6abcf9Millán, María S9fe60bec-aad5-4e2e-99bd-db4b5e8f4a1bMarrugo, Andrés G.00746131-f46c-4d8c-9c02-514385d7b36e2023-07-18T19:17:34Z2023-07-18T19:17:34Z2021-09-022023-07Barrios, E., Pineda, J., Romero, L.A., Millán, M.S., Marrugo, A.G. Skin color correction via convolutional neural networks in 3D fringe projection profilometry (2021) Proceedings of SPIE - The International Society for Optical Engineering, 11804, art. no. 118041P, . DOI: 10.1117/12.2594331https://hdl.handle.net/20.500.12585/1211410.1117/12.2594331Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarFringe Projection Profilometry (FPP) with Digital Light Projector technology is one of the most reliable 3D sensing techniques for biomedical applications. However, besides the fringe pattern images,often a color texture image is needed for an accurate medical documentation. This image may be acquired either by projecting a white image or a black image and relying on ambient light. Color Constancy is essential for a faithful digital record, although the optical properties of biological tissue make color reproducibility challenging. Furthermore, color perception is highly dependent on the illuminant. Here, we describe a deep learning-based method for skin color correction in FPP. We trained a convolutional neural network using a skin tone color palette acquired under different illumination conditions to learn the mapping relationship between the input color image and its counterpart in the sRGB color space. Preliminary experimental results demonstrate the potential for this approach.application/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2Proceedings of SPIE - The International Society for Optical Engineering - Vol. 11804 (2021)Skin color correction via convolutional neural networks in 3D fringe projection profilometryinfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Color constancyConvolutional neural networkImage color processingMachine learningSkin color correctionCartagena de IndiasMarrugo, A.G., Gao, F., Zhang, S. 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Cited 4 times. http://en.sv-jme.eu/data/upload/2015/05/01_2015_2424_Lalos_04.pdf doi: 10.5545/sv-jme.2015.2424Pineda, J., Vargas, R., Romero, L.A., Marrugo, J., Meneses, J., Marrugo, A.G. Robust automated reading of the skin prick test via 3D imaging and parametric surface fitting (2019) PLoS ONE, 14 (10), art. no. e0223623. Cited 7 times. https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0223623&type=printable doi: 10.1371/journal.pone.0223623Rey-Barroso, L., Burgos-Fernández, F.J., Ares, M., Royo, S., Puig, S., Malvehy, J., Pellacani, G., (...), Ricart, M.V. Morphological study of skin cancer lesions through a 3D scanner based on fringe projection and machine learning (2019) Biomedical Optics Express, 10 (7), pp. 3404-3409. Cited 6 times. https://www.osapublishing.org/boe/viewmedia.cfm?uri=boe-10-7-3404&seq=0 doi: 10.1364/BOE.10.003404Xu, J., Zhang, S. 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