The three-dimensional (3D) reconstruction of objects has been well studied in the literature of augmented reality (AR) [1, 2]. Most existing studies have assumed that the to-be-constructed target object is rigid, whereas objects in the real world can be dynamic or deformable. Therefore, AR systems are required to deal with non-rigid objects to be adaptive to environmental changes. In this paper, we address the problem of reconstructing articulated objects as a starting point for modeling deformable objects. An articulated object is composed of partially rigid components linked with joints. After building a mesh model of the object, the model is segmented into the components along their boundaries by a graph-cut-based approach that we propose.

This paper describes a new learning by observation algorithm based on Bayesian networks and game pattern graphs. Even with minimal knowledge of a game or human instructions, the robot can learn the game rules by watching human demonstrators repeatedly play the game multiple times. Based on the knowledge acquired from this learning process, represented in Bayesian networks and game pattern graphs, the robot can play games as robustly as humans do. Our learning algorithm for human-robot game interaction is implemented using a teddy bear-like robot and is demonstrated by application to well-known social games, specifically Rock-Paper-Scissors, Muk-Chi-ba and Blackjack.

In an environment where the contexts of users are complex and the degree of freedom of user activity is very high, such as in daily life, several factors need to be considered for constructing user models. Such a model should include changes in the meanings of activities that reflect the user's situation both temporally and individually. In this paper we propose a novel approach for personalizing the user model and adapting it to individual circumstances with a wearable sensor network. We also describe the process for determining the repetitive activities of a user by using incremental clustering and Bayesian network. We show experimental results for an adaptive user model based on a real wearable sensor platform. Multimedia data of user experience are acquired from the multimodal sensors, and processed to metadata that have meanings.