In this paper, we propose the illusion-based "Psuedo-gustation" method to change perceived taste of a food when people eat by changing its appearance and scent with augmented reality technology. We aim at utilizing an influence between modalities for realizing a "pseudo-gustatory" system that enables the user to experience various tastes without changing the chemical composition of foods. Based on this concept, we built a "Meta Cookie+" system to change the perceived taste of a cookie by overlaying visual and olfactory information onto a real cookie. We performed an experiment that investigates how people experience the flavor of a plain cookie by using our system. The result suggests that our system can change the perceived taste based on the effect of the cross-modal interaction of vision, olfaction and gustation.

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The main contribution of this paper is to realize computer generated augmented flavors and establish a method to integrate gustatory information into computer human interactions. There are several reasons for the scarcity of research on gustatory information. One reason is that taste sensations are affected by a number of factors, such as vision, olfaction and memories. This produces a complex cognition mechanism for a user's gustatory sensation, and makes it difficult to build up a gustatory display which produces a specific taste on demand.Our hypothesis is that the complexity of gustatory sensation can be applied to the realization of a "Pseudo-gustatory" display that presents the desired flavors by means of a cross-modal effect elicited by visual and olfactory augmented reality. We propose the Edible Marker system, which can detect the state [number/shape/6-degree-offreedom (DOF) coordinate] of each piece of bitten or divided food in real time, and the "Pseudo-gustation" method to change the perceived taste of food by changing its appearance and scent. We construct "MetaCookie+" as an implementation and discuss its validity through an exploratory study.

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Several indoor positioning systems have been studied to offer a public service based on one's location information. We have studied an indoor localization system using original fiducial markers. The original markers can be designed freely by users; therefore, they can be used for interior decoration. Using this system, you can obtain the three-dimensional position and pose of your camera by capturing the image of the markers arranged on the floor. However, there are some problems associated with the use of this system in a public space, such as a decline in the marker recognition rate by a change in the surrounding light condition and an instability in the marker recognition rate depending on the type of marker used and the camera angle. In this study, we enhanced the robustness of an indoor localization system used in a public space and increased the number of recognizable markers.

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The use of large visual displays in public spaces such as large buildings has become increasingly popular. Public art can make use of the characteristics and context of the site. However, it is difficult to install new displays in existing buildings because of the large, rigid hardware associated with such displays. In this article, we describe a robust, lightweight, low-profile, and fully restorable display system that can be easily and quickly installed for use in existing public buildings. We considerably reduced the number of physical components and the system weight with our proposed method, which can be optimized for any planned content. We describe the technical design and implementation of the display system and discuss some of its applications for public audiences. We then report a three-month field trial that we conducted at an airport terminal building. We discuss the advantages and effectiveness of this system in light of the field trial results.

In this research study, we propose a divided planar-object detection method for augmented reality(AR) applications. There are mainly two types of camera-registration methods for AR applications: marker-based methods, and natural-feature-based methods. In addition, the latter methods are classified into visual SLAM and object detection methods. With respect to object detection methods, particularly for planar objects such as paper, methods for dealing with bending, folding, and occlusion are proposed. However, the division of objects has not been studied. Once an object is divided, a conventional object detection method cannot identify each of the pieces because the feature points of only a single piece are recognized as the target object, and the other feature points are regarded as outliers.The proposed system prepares a database of the target object's natural features, and applies progressive sample consensus(PROSAC), which is a robust estimation method, for iterative homography calculation to achieve the multiple planar-object detection. Moreover, the proposed method can detect shapes of pieces by simultaneously using an occlusion detection method. We demonstrate that it is possible to interact with an arbitrarily divided planar object in real time by our method to implement some AR applications.