The technology used to detect community structures in graphs, or graph clustering technology, is important in a wide range of disciplines, such as sociology, biology, and computer science. Previously, many successful community detection methods have relied on the optimization of a quantity referred to as modularity, which is a quality index for the partition of a graph into communities. However, such methods suffer from a key drawback, namely, the inability to identify relatively small communities. To overcome this drawback, we propose a novel community detection method that can detect small communities. This is based on the property that a random walker will not readily leave a community even if it is small. The work presented in this paper demonstrates that our method detects both small and large communities in the practical application of clustering tourist attraction images obtained from Flickr.

In our daily life, the interactive roles of leaders, followers, and coordinators tend to emerge from multiparty collaboration. The primary purpose of this study is to automatically predict the leading role in multiparty interaction by ubiquitous computing techniques. Even though the leading role has been predicted for an entire task, there has been little focus on evaluating how roles are reorganized during a task. To find the verbal and nonverbal cues that might predict roles, we asked neutral third parties to select the participant playing the leading role in an assembly task. We examined the correlation between behavioral data gathered during a task and third-party evaluations of the leading role player in terms of temporal alterations. The preliminary results suggest that task-oriented utterances and verification behaviors regarding progress status contribute to the prediction of the emerging and reorganized leader. Moreover, we discuss the implications of our findings for the design of applications that can enhance multiparty collaboration.

Commercially available motion capture products give us fairly precise movements of human body segments but do not measure enough information to define skeletal posture in its entirety. This sketch describes how to obtain the complete posture of skeletal structure with the help of marker locations relative to bones that are derived from MRI data sets.

This paper proposes a new real-time method for estimating human postures in 3D from trinocular images. In this method, an upper body orientation detection and a heuristic contour analysis are performed on the human silhouettes extracted from the trinocular images so that representative points such as the top of the head can be located. The major joint positions are estimated based on a genetic algorithm-based learning procedure. 3D coordinates of the representative points and joints are then obtained from the two views by evaluating the appropriateness of the three views. The proposed method implemented on a personal computer runs in real-time. Experimental results show high estimation accuracies and the effectiveness of the view selection process.

Multi-media ambiance communication refers to a means of shared-space communication, that makes use of actual moving pictures captured by video camera, combined with the laws of perspective, as used in painting and the visual characteristics of human beings, to establish a photo-realistic quality three-dimensional image space that users can naturally feel part of. We aim to enable this ambiance communication by basing the shared-space on actual moving pictures rather than on an accurate three-dimensional image space, such as is used in computer graphics.

This paper introduces a new real-time method to estimate the posture of a human from thermal images acquired by an infrared camera regardless of the back-ground and lighting conditions. Distance transformation is performed for the human body area extracted from the thresholded thermal image for the. Calculation of the center of gravity. After the orientation of the upper half of the body is obtained by calculating the moment of inertia, significant points such as the top of the head, the tips of the hands and foot are heuristically located. In addition, the elbow and foot positions are estimated from the detected (significant) points using a genetic algorithm based learning procedure. The experimental results demonstrate the robustness of the proposed algorithm and real-time (faster than 20 frames per second) performance.