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We propose a layout estimation method for multi-layered ink using a measurement of the line spread function (LSF) and machine learning. The three-dimensional printing market for general consumers focuses on the reproduction of realistic appearance. In particular, for the reproduction of human skin, it is important to control translucency by adopting a multilayer structure. Traditionally, layer design has depended on the experience of designers. We, therefore, developed an efficient layout estimation to provide arbitrary skin color and translucency. In our method, we create multi-layered color patches of human skin and measure the LSF as a metric of translucency, and we employ a neural network trained with the data to estimate the layout. As an evaluation, we measured the LSF from the computer-graphics-created skin and fabricate skin using the estimated layout; evaluation with root-mean-square error showed that we can obtain color and translucency that are close to the target.

In this article, the authors propose a method to estimate the ink layer layout for a three-dimensional (3D) printer. This enables 3D printed skin to be produced with the desired translucency, which they represent as line spread function (LSF). A deep neural network in an encoder–decoder model is used for the estimation. It was previously reported that machine learning is an effective way to formulate the complex relationship between optical properties such as LSF and the ink layer layout in a 3D printer. However, although 3D printers are more widespread, the printing process is still time-consuming. Hence, it may be difficult to collect enough data to train a neural network sufficiently. Therefore, in this research, they prepare the training data, which is the correspondence between an LSF and the ink layer layout in a 3D printer, via computer simulation. They use a method to simulate the subsurface scattering of light for multilayered media. The deep neural network was trained with the simulated data and evaluated using a CG skin object. The result shows that their proposed method can estimate an appropriate ink layer layout that closely reproduces the target color and translucency.

We developed a system to improve the quality of telemedicine, and the test results obtained have been presented in this paper, along with the technical details of the system. The spread of COVID-19 has accelerated the need for telemedicine to effectively prevent infections. However, in traditional Japanese medicine (Kampo), where color is essential, an accurate diagnosis cannot be made without color reproduction. Because commercial smartphones cannot reproduce colors with the level of fidelity required for medical treatments, we created a color chart that includes the human skin and tongue colors to help doctors identify their colors accurately during a telemedicine examination. Further, we developed a telemedicine system that allows for automatic color correction using a mobile device, with a color chart and non-contact heart rate measurements.

In this paper, we propose a layout estimation method for multi-layered ink by using PSF measurement and machine learning. This estimation can bring various capabilities of color reproduction for the newfangled 3D printer that can apply multi-layered inkjet color. Especially, the control of translucency is useful for the reproduction of skin color that is overpainted flesh color on bloody-red layer. Conventional method of this layer design and color selection depended on the experience of professional designer. However, it is difficult to optimize the color selection and layer design for reproducing complex colors with many layers. Therefore, in this research, we developed an efficiency estimation of color layout for human skin with arbitrary translucency by using machine learning. Our proposed method employs PSF measurement for quantifying the color translucency of overlapped layers. The machine learning was performed by using the correspondence between these measured PSFs and multi-layered printings with 5-layer neural network. The result was evaluated in the CG simulation with the combination of 14 colors and 10 layers. The result shows that our proposed method can derive an appropriate combination which reproduce the appearance close to the target color and translucency.

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In this paper, we propose a method to estimate ink layer layout used as an input for 3D printer. This method makes it possible to reproduce a 3D printed patch that gives a desired translucency, which is represented as Line Spread Function (LSF) in this study. Deep neural networks of encoder decoder model is used for the estimation. In a previous research, it is reported that machine learning method is effective to formulate the complex relationship between the optical property such as LSF and the ink layer layout in 3D printer. However, it may be difficult to collect data large enough to train a neural network sufficiently. Especially, although 3D printer is getting more and more widespread, the printing process is still time consuming. Therefore, in this research, we prepare the training data, which is the correspondence between LSF and ink layer layout in 3D printer, by simulating it on a computer. MCML was used to perform the simulation. MCML is a method to simulate subsurface scattering of light for multi-layered media. Deep neural network was trained with the simulated data, and evaluated using a CG skin object. The result shows that our proposed method can estimate an appropriate ink layer layout which reproduce the appearance close to the target color and translucency.