We present a 3D phenotyping platform that measures both plant growth and environmental information in small indoor environments for plant image datasets. Our objective is to construct a compact and complete platform by using commercial devices to allow any researcher to begin plant phenotyping in their laboratory. In addition, we introduce our annotation tool to manually but effectively create leaf labels in plant images on a pixel-by-pixel basis. Finally, we show our RGB-D and multiview datasets containing images in the early growth stages of the Komatsuna with leaf annotation.

We propose a method for create a background model in non-stationary scenes. Each pixel has a dynamic Gaussian mixture model. Our approach can automatically change the number of Gaussians in each pixel. The number of Gaussians increases when pixel values often change because of Illumination change, object moving and so on. On the other hand, when pixel values are constant in a while, some Gaussians are eliminated or integrated. This process helps reduce computational time. We conducted experiments to investigate the effectiveness of our approach.

A sign language recognition system is required to use information from both global features, such as hand movement and location, and local features, such as hand shape and orientation. We present an adequate local feature recognizer for a sign language recognition system. Our basic approach is to represent the hand images extracted from sign-language images as symbols which correspond to clusters by a clustering technique. The clusters are created from a training set of extracted hand images so that a similar appearance can be classified into the same cluster on an eigenspace. The experimental results indicate that our system can recognize a sign language word even in two-handed and hand-to-hand contact cases.

This paper presents a real-time motion capture system of 3D multi-part objects, whose purpose is to do seamless mapping of objects in the real world into virtual environments easily. In general, virtual environment applications such as man-machine seamless interaction require the system to estimate accurate motion parameters at real-time for natural objects such as human bodies. To achieve this requirement, we have been developing a vision-based motion capture system which reconstructs time-varying motion parameters of 3D multi-part objects. The advantage of such a vision-based system is that it is possible to acquire the other scene parameters such as shape and surface properties at the same time, using the same equipment in measuring motion. In this paper, as our first system, we have implemented a color-marker-based motion capture system which realizes multi-view fusion and have demonstrated our motion capture and reconstruction system works at real-time on PC-clusters.