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Takuji NARUMI
Session ID: 23-01-01
Published: 2023
Released on J-STAGE: February 20, 2025
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With the widespread use of head-mounted displays and motion tracking technology, it has become possible to immerse oneself in a virtual reality or metaverse using an avatar, which is a body in a virtual environment, and obtain various physical experiences. It has been shown that the physical experiences obtained by immersing in an avatar with characteristics different from those of the actual body can alter the user's self-perception, and it leads to alter our perceptions, behaviors, abilities, and even thoughts. The speaker has been conducting research on “Ghost Engineering,” a technology that supports a flexible mind by actively utilizing such effects. In the course of the research, it became clear that in order for Ghost Engineering to be accepted by users and society, it is necessary not only to pursue technologies that cause temporary self-transformation and human augmentation, but also to consider technologies that support users in giving appropriate narratives and meanings to such expanded selves in their lives. In this paper, I discuss the importance of considering the “narrative self” when considering human augmentation in the society, introducing examples of research to clarify the relationship between tentative transformation and the narrative self using avatars and future prospects.
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Koudai Nagaishi, Makoto FUJISAWA, Masahiko MIKAWA
Session ID: 23-01-02
Published: 2023
Released on J-STAGE: February 20, 2025
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In this paper, we propose a visual simulation method for underwater explosions considering water surface changes
based on Shallow Water Simulation using the particle method. The shock wave is simulated by solving the shock wave equation
using a combination of the Adomian decomposition method (an analytical method) and the semi-Lagrangian method (a numerical
method), and the method adds the representation of splashes by 3D particles to Shallow Water Simulation, which can simulate
fluids at high speed. We propose a method to reproduce changes in the water surface caused by explosions. The effectiveness of
the proposed method was verified by implementing it and reproducing an underwater explosion scene in CG as an experiment.
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Ryuichi TOZAKI, Yuma NISHINO, Youngha CHANG, Nobuhiko MUKAI
Session ID: 23-01-03
Published: 2023
Released on J-STAGE: February 20, 2025
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Waterfalls exhibit various kind of behavior, such as water stream flowing from the mouth of waterfalls, splashing water reflected from rocky areas, and water spray rising from the waterfall basin. The behavior is greatly affected by the geographical features around the waterfalls. Therefore, we have attempted to represent a waterfall with the same geographical feature made by CG as a real waterfall “Kegon Falls” by the simulation combined with particle and grid methods.
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Nobutaka NATSUI, Hisaki NATE, Kazuo ISHIKAWA
Session ID: 23-01-04
Published: 2023
Released on J-STAGE: February 20, 2025
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When binocular stereoscopic images are captured and observed using live-action or computer graphics, the depth distance of
stereoscopic objects may be affected by patterns on the surface of the objects. In this experiment, we measured whether the depth perception
of stereoscopic objects changes when the patterns (textures) of the stereoscopic targets are changed. The experiment was conducted under
three conditions: two types of random dots (3 Pix, 5 Pix) and a camouflage pattern that was drawn in a somewhat random and continuous
manner. Parallel and crossed photography were used. The experimental results showed that each texture tended to converge to a value close
to the logical value, and the random dots (3Pix) showed relatively small variation.
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Masataka TOZUKA, Kunihiko TAKANO, Koki SATO
Session ID: 23-01-05
Published: 2023
Released on J-STAGE: February 20, 2025
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Holographic memory uses a lens term to perform multiple image separation, and research has been carried out to improve the degradation of the reconstructed image through image processing. However, there are many problems due to the use of optics. Therefore, it is necessary to compare simulations with optical experiments, and this paper presents simulations of digital holography kinoforms and holograms. It was possible to perform recording, regenerated image, multiple image separation using lens terms and noise removal by image processing.
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Koki INOUE, Atsushi KOIKE
Session ID: 23-01-06
Published: 2023
Released on J-STAGE: February 20, 2025
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In recent years, research on image processing using deep learning has been active, and an image coding method using neural networks has been proposed. In this paper, we propose a video coding method using an auto encoder. The encoding performance is improved by learning the decoded image of the previous frame during encoding, and video coding is performed. In this study, we conducted computer experiments using 4K video images to compare and evaluate two methods, one using a new autoencoder when learning decoded images and the other using a learned autoencoder. Compared to coding with a learned model, this method can improve the image quality with each successive frame.
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Yoshiki Nakamura, Tomoya Hirabayashi, Shuhei Kodama, Tomoaki Moriya, T ...
Session ID: 23-01-07
Published: 2023
Released on J-STAGE: February 20, 2025
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Dynamic visual acuity (DVA), which is necessary for sports, driving, and games, tends to decline with age. Regular
eye training is necessary to maintain dynamic visual acuity. In this study, a pachinko-type slot machine requires excellent
dynamic visual acuity when performing the act of “eye-pushing”. Inspired by a pachinko slot machine, we have developed an
eye-pushing practice system. Experiments verified that “eye pressing” practice is effective in improving DVA.
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Mikoto Okano, Shuhei Kodama, Tomoaki Moriya, Tokiichiro Takahashi
Session ID: 23-01-08
Published: 2023
Released on J-STAGE: February 20, 2025
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In recent years, accidents caused by road rage have attracted attention. One of the ways to deal with road rage is
to simulate several scenarios of road rage and simulate how to deal with them. In this paper, we report on the development of an
experience-based driving simulator that allows you to drive with tailgate.
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Kunio Ohno
Session ID: 23-01-09
Published: 2023
Released on J-STAGE: February 20, 2025
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Digital museums have the merit of eliminating the spatial restrictions of real museums through digitizing
the exhibits and collections of museums by the Web. On the other hand, it is not suitable for giving the
strong impression that a real museums have. The background has been studied from the viewpoint of Walter
Benjamin’s aura. Furthermore, based on the results of a study on the social role of the Yokosuka City Museum
and the Hiratsuka City Museum, the possibility that a digital museum converted into web content can play the
role of a real museum in learning, education, and research.
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－Trial of Restoration from Peripheral Pixel Information using Spatial Filtering－
Riko ABE, Yuya DAN
Session ID: 23-01-10
Published: 2023
Released on J-STAGE: February 20, 2025
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As the performance of computer systems improves, image recognition technology expands its scope of application and is being implemented in society in a variety of fields. For example, it is one of the important applications to recognize the information about the product from the image of the product displayed in the retail store. However, when recognizing a product and extracting the necessary information, the specular reflection of light from the surface of the package caused by light sources such as ceiling lights acts as noise, reducing the accuracy of computer image recognition. Therefore, the specular reflection can be removed simply by applying a spatial filter to a single image, in order to improve the accuracy of image recognition. In the article, we describe the results of an experiment that improve the accuracy of image recognition.
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Kouki MAEDA, Youngha CHANG, Nobuhiko MUKAI
Session ID: 23-01-11
Published: 2023
Released on J-STAGE: February 20, 2025
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In recent years, there are many studies that focuses on human region extraction and attribute recognition in images and
videos. In clothing attribute recognition, there are many researches attempting to recognize the types and colors of clothing , as well as pattern
recognition as one of the attributes in multi task classification. However, the accuracy of pattern recognition is still low. Nevertheless, pattern
information is considered an important attribute since it is frequently used in clothing descriptions, as well as type and color. Therefore this
study proposes a recognition system of the pattern of upper body clothing using deep learning. Our approach involves extracting the upper
body clothing area from an image using MediaPipePose, followed by classification into five pattern categories utilizing a trained VGG16
model through transfer learning. We have performed the evaluation and confirmed that the average recognition rate achieved over 90%.
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Mirai AMO, Youngha CHANG, Nobuhiko MUKAI, Yasuzo TANAKA
Session ID: 23-01-12
Published: 2023
Released on J-STAGE: February 20, 2025
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In this study, we propose a method to measure the number of people based on a bird’s-eye movie. Local Binary Patterns (LBP) features are calculated for the input video, and the person area is estimated based on the changes of LBP features between frames. Using the estimated person area as a starting region, a region expansion method using background difference and RGB values is applied to obtain the person area. If the person area is composed of multiple people, each individual is detected using the past detection results and the K-means method. The moving direction of the passing people is determined based on the passing order on two virtual lines. As the result of the experiments, we have confirmed the effectiveness of the people detection and the measurement methods.
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－Visualization of features using factor information common to clusters－
Haruya Tanaka, Chanjin Seo, Jun Ohya, Hiroyuki Ogata
Session ID: 23-01-13
Published: 2023
Released on J-STAGE: February 20, 2025
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In recent years, the running population has been increasing, and demand for coaching systems for amateur runners is expected to increase. Since each individual has a different goal and body shape, it is necessary to provide more personalized coaching, which requires quantitative evaluation of the characteristics of individual movements. As a first step, this paper examines a method for extracting and showing characteristic shapes common to clusters from still image data of common footwear using deep metric learning. In our experiments, we were able to show the common characteristic shapes for the entire cluster based on the common features in the set according to the distance from the cluster center. The application of the experimental results to running behavior is discussed.
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