In recent years, along with the aging of society worldwide, decrease of working population has become a serious problem. For this reason, robots are expected to substitute human work, automate distribution, and support human daily life, especially, elderly care assistance. In this research, we focus on the support of those who need care in everyday life, and, in this paper, we propose a human-robot cooperative system that supports acquisition of objects in cooperation with a human. The outline of the action of this robot is as follows: (i) It moves to the location designated by a user autonomously (not by remote control): (ii) On arrival, the robot exchanges information by video with the user who is at a remote place, and acquires the objects designated by the user among those placed there. (iii) After the acquisition, the robot moves again autonomously to the user and hands over the objects to the user. In this paper, we focus on step (ii) and show methods and some experimental results.
Recently, along with rapid development of the image processing technology, image processing has been adopted in various fields for various purposes. Development of an intelligent machine that mounts a camera as an eye is a thriving technology, and it is employed not only in industrial fields but also in the fields involving ordinary citizens. Especially, development of Intelligent Transportation Systems is very active and many methods of detecting human and automobiles have been proposed using laser radars, LIDARs or in-vehicle cameras. However, they remain only on the detection of the presence of such objects and the methods to detect rush-out objects into a road have not been developed yet. In this paper, a method is proposed which detects a human from an image with his/her body direction information. This intends to detect a human who might rush out into a road in front of an ego-car. In order that the human model used for extracting the feature may capture the appearance of human rush-out properly, directional human models and classifiers are introduced. The proposed method was examined its performance experimentally and the effectiveness of the method was shown/ satisfactory results were obtained.
To obtain an effective interpretation of organic shape using statistical shape models (SSMs), the correspondence of the landmarks through all the training samples is the most challenging part in model building. In this study, a coarse-tofine groupwise correspondence method for 3-D polygonal surfaces is proposed. We manipulate a reference model in advance. Then all the training samples are mapped to a unified spherical parameter space. According to the positions of landmarks of the reference model, the candidate regions for correspondence are chosen. Finally we refine the perceptually correct correspondences between landmarks using particle filter algorithm, where the likelihood of local surface features are introduced as the criterion. The proposed method was performed on the correspondence of 9 cases of left lung training samples. Experimental results show the proposed method is flexible and under-constrained.
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