Hironori Shigeta

Owens Luis — A context-aware multi-modal smart office chair in an ambient environment

This paper introduces a smart office chair, Owens Luis, whose pronunciation has a meaning of “an encouraging chair (****)” in Japanese. For most of the people, office environments are the place where they spend the longest time while awake. To improve the quality of life (QoL) in the office, Owens Luis monitors an office workers mental and physiological states such as sleepiness and concentration, and controls the working environment by multi-modal displays including a motion chair, a variable color-temperature LED light and a hypersonic directional speaker.

Parallelization of Particle Based Volume Rendering on Tiled Display Wall

In this paper we present a parallelized particle-based volume rendering (PBVR) system to obtain super linear speedup, i.e. the speedup with N processors is greater than N, on a distributed computing system. PBVR is a technique of volume rendering which does not need any sorting therefore it is suitable for parallel execution. We implemented the parallel version by using Open CABIN middleware and measured the performance. We confirmed that the parallelized PBVR is 1.5 to 8 times faster than the serial version.

Bone marrow cavity segmentation using graph-cuts with wavelet-based texture feature

Emerging bioimaging technologies enable us to capture various dynamic cellular activities [Formula: see text]. As large amounts of data are obtained these days and it is becoming unrealistic to manually process massive number of images, automatic analysis methods are required. One of the issues for automatic image segmentation is that image-taking conditions are variable. Thus, commonly, many manual inputs are required according to each image. In this paper, we propose a bone marrow cavity (BMC) segmentation method for bone images as BMC is considered to be related to the mechanism of bone remodeling, osteoporosis, and so on. To reduce manual inputs to segment BMC, we classified the texture pattern using wavelet transformation and support vector machine. We also integrated the result of texture pattern classification into the graph-cuts-based image segmentation method because texture analysis does not consider spatial continuity. Our method is applicable to a particular frame in an image sequence in which the condition of fluorescent material is variable. In the experiment, we evaluated our method with nine types of mother wavelets and several sets of scale parameters. The proposed method with graph-cuts and texture pattern classification performs well without manual inputs by a user.

A bone marrow cavity segmentation method using wavelet-based texture feature

A better understanding of in vivo bio images is expected to contribute to the discovery of new drugs and mechanisms of disease. To improve the contributions of in vivo bioimaging, the extraction of a particular region is required in order to detect a particular cells motion because manual image processing of a massive number of images is unrealistic. One of the issues for automatic image-segmentation is that conditions of image-taking are variable. Thus, some manual input and/or manual tuning of some parameters is required to adjust each image. To reduce manual operation for image processing of bone marrow cavity segmentation, we focused on the texture pattern of bone marrow cavity. In this paper, we propose a bone marrow cavity segmentation method using support vector machine and wavelet-based texture feature. The proposed method does not require manual inputs to obtain distribution of intensity before processing, because the texture patterns of bone marrow cavity regions are integrated into the system in advance. Moreover, it is applicable to a particular frame in an image sequence in which the condition of fluorescent material is variable because it does not require temporal variation or initial frame for the segmentation. In the experiment, we evaluated our method with nine types of mother wavelets and several sets of scale parameters. The bone marrow cavity segmentation, using graph-cuts with our texture pattern classification, performs well without manual inputs by a user.