Robotic automation is expected to be applicable in various fields. The utilization of robots requires human-robot interaction (HRI) for prolonged direct manipulation or learning. Recently, numerous studies on HRI have been conducted in virtual space using virtual, augmented, and mixed reality (VAM-HRI). In the future, VAM-HRI applications are expected to involve users wearing head-mounted displays (HMDs). However, HMDs present various problems, such as vergence and accommodation conflict (VAC) caused by eye strain, and thus cannot be used for long periods. In this study, a remote robot operation interface is developed to solve this problem. Experiments are conducted from the viewpoint of ophthalmology to determine whether “removal of VAC” and “improvement of depth perception” by Light Field HMDs (LFHMD), and “reduction of eye strain” by TransRay are feasible. The results reveal that the LFHMD is superior to the general HMDs for all items.

Abstract Digitization of cultural assets has become an important sub-area of computer vision (CV). Thus far, the value of digitization has been emphasized in terms of asset preservation and exhibition. The third aspect of digitization value is that the obtained digital data can be used to perform archaeological analysis based on physics and optics theories and simulations. This position paper emphasizes the importance of this third aspect, using our Kyushu decorative tumuli project as an illustrative example. In particular, we focus on the photometric approaches in the third aspect and explain the equipment and methods developed there as well as archaeological findings. This paper, then, proposes to establish this area as “cyber-archaeology” through categorizing and organizing those methodologies.

Here, we propose a novel method to estimate the parameters of non-planar objects with thin film surfaces. Being able to estimate the optical parameters of objects with thin film surfaces has a wide range of applications from industrial inspections to biological and archaeology research. However, there are many challenging issues that need to be overcome to model such parameters. The appearance of thin film objects is highly dependent on the surface orientation and optical parameters such as the refractive index and film thickness. First, we therefore analyzed the optical parameters of non-planar objects with thin film surfaces. Next, we proposed and implemented an analysis procedure and demonstrated its effectiveness for studying planar objects with thin film surfaces. Finally, we developed a device to acquire the shapes and optical parameters of objects with thin film surfaces using a camera and demonstrated the effectiveness of our method experimentally. Then, we surveyed the errors caused by the light source. We discussed the difference between the theoretically obtained parameters and experimental data obtained using a hyper spectral camera.

Reconstruction of shapes and appearances of thin film objects can be applied to many fields such as industrial inspection, biological analysis, and archaeologic research. However, it comes with many challenging issues because the appearances of thin film can change dramatically depending on view and light directions. The appearance is deeply dependent on not only the shapes but also the optical parameters of thin film. In this paper, we propose a novel method to estimate shapes and film thickness. First, we narrow down candidates of zenith angle by degree of polarization and determine it by the intensity of thin film which increases monotonically along the zenith angle. Second, we determine azimuth angle from occluding boundaries. Finally, we estimate the film thickness by comparing a look-up table of color along the thickness and zenith angle with captured images. We experimentally evaluated the accuracy of estimated shapes and appearances and found that our proposed method is effective.

Many object surfaces are composed of layers of different physical substances, known as layered surfaces. These surfaces, such as patinas, water colors, and wall paintings, have more complex optical properties than diffuse surfaces. Although the characteristics of layered surfaces, like layer opacity, mixture of colors, and color gradations, are significant, they are usually ignored in the analysis of many methods in computer vision, causing inaccurate or even erroneous results. Therefore, the main goals of this paper are twofold: to solve problems of layered surfaces by focusing mainly on surfaces with two layers (i.e., top and bottom layers), and to introduce a decomposition method based on a novel representation of a nonlinear correlation in the color space that we call the “spider” model. When we plot a mixture of colors of one bottom layer and n different top layers into the RGB color space, then we will have n different curves intersecting at one point, resembling the shape of a spider. Hence, given a single input image containing one bottom layer and at least one top layer, we can fit their color distributions by using the spider model and then decompose those layered surfaces. The last step is equivalent to extracting the approximated optical properties of the two layers: the top layer's opacity, and the top and bottom layers' reflections. Experiments with real images, which include the photographs of ancient wall paintings, show the effectiveness of our method.