Skin color correction via convolutional neural networks in 3D fringe projection profilometry

Erik Barrios; Jesus Pineda; Lenny A. Romero; María S. Millán; Andres G. Marrugo

doi:10.1117/12.2594331

2 September 2021 Skin color correction via convolutional neural networks in 3D fringe projection profilometry

Erik Barrios, Jesus Pineda, Lenny A. Romero, María S. Millán, Andres G. Marrugo

Proceedings Volume 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021; 118041P (2021) https://doi.org/10.1117/12.2594331
Event: SPIE Nanoscience + Engineering, 2021, San Diego, California, United States

Abstract

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.

Conference Presentation

Citation Download Citation

Erik Barrios, Jesus Pineda, Lenny A. Romero, María S. Millán, and Andres G. Marrugo "Skin color correction via convolutional neural networks in 3D fringe projection profilometry", Proc. SPIE 11804, Emerging Topics in Artificial Intelligence (ETAI) 2021, 118041P (2 September 2021); https://doi.org/10.1117/12.2594331

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