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In order to communicate with a person in a remote place, there are many means such as sending sentences, making a phone call, chatting by video. A contact system with a distant person becomes a communication tool through an avatar by a virtual reality system, and we feel that there is a barrier to reality. So, we build a system to deliver virtual objects to a user in remote place by behaving as if handing the objects. Remote and present space views are projected on a wall using video chat, and each virtual object is handed over by using an Augmented Reality (AR) marker. The system promotes communication by feeling the connection of the space in a remote place.

It has hitherto been difficult to treat landscape images in a database, since their physical characteristics have been described empirically. This paper analyzes the correlation between the physical characteristics (colors, including their brightness, directions, and positions) and their associated adjectives, so that a landscape image can be modeled by using the adjectives. As a result, landscape images can be evaluated automatically, and an unknown landscape image can be retrieved and evaluated. Experiments have been carried out to show the usefulness of the proposed method. © 2001 Scripta Technica, Syst Comp Jpn, 32(6): 62–71, 2001

Higher order local autocorrelation (HLAC) features are used in various 2D image applications. Recent three dimensional extensions of HLAC features (3D HLAC) have made possible the analysis of solid textures including solid textures. Typically, 3D HLAC features are computed by a limited number of three dimensional mask patterns, and the order of the mask patterns is limited up to the second order. In this paper, the orders of the mask patterns are increased up to the 26th order. Since there are a large number of combinations of mask patterns, we have implemented a simulation program for finding all their combinations in a systematic manner. Also the various subsets of the 3D HLAC mask patterns are computed by limiting the displacement regions of the masks. The 3D HLAC features were computed as local features rather than global features, and the bag of features (BoF) approach was applied for comparing local features. Our experimental program extracted pattern features from solid textures by using the extended 3D HLAC mask patterns. In our preliminary experiments, databases of solid textures were synthesized from 2D images. A similarity retrieval system for the solid textures has been implemented, and its extension to the 3D HLAC features performed well in retrievals.

Our hypothesis is that the model of subjective interpretation is based on rough information process. We analyze a relationship between adjectives and three graphical attributes of color, direction, and position in order to model the subjective interpretation for streetscape images. We focus especially on the graphical descriptors such as Lab color space, Lab auto-correlation and rough image segmentation, which correspond to the graphical attributes. Our psychological experiment indicates that subjective interpretations consist partly of “evaluation”, “texture”, and “openness”. By analyzing the relationships between these three factors and the graphical descriptors, we find that subjective interpretation relates to the combination of the three graphical descriptors.

We analyze and model the interaction of movement of the synchronized auditory‐visual information. We construct the relationship of movement between virtually sound source and object. We suppose that cubic local contrast model (CLCM) we propose and the optical flow method are useful for analyzing moving image. CLCM is a method that describes the movement of objects from temporal and spatial difference of local contrast between pixels in moving image, and we found CLCM can measure the orientation of camera motion and the moving direction of an object in a moving image. We use the feature of head‐related transfer function (HRTF) as the feature of sound movement. We have done two experiments: experiment 1 is that we analyze the data of the interaural amplitude difference (IAD) and interaural phase difference (IPD) between right and left HRTFs of a sphere by local contrast model (LCM); experiment 2 is that we analyze the relationship between the movement of an object in a moving image and a virtual sound source. We discuss the interactive features between auditory and visual information.