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In the field of the interface research, confusion had been discussed since before. However, the research that focused on link of interface and confusion motion is very little. In contrast, in the field of the cognitive research, a lot of study about confusion is researching as a popular theme since before. These studies use video camera or motion capture system as a method to observation and analysis...
In previous work, the authors have been developing a stochastic model based approach for on-line segmentation of whole body human motion patterns during human motion observation and learning, using a simplified kinematic model of the human body. In this paper, we extend the proposed approach to larger, more realistic kinematic models, which can better represent a larger variety of human motions. These...
This paper proposes a stochastic approach for representing and analyzing the gradual changes that occur in human movement during sports training. Human movement primitives are described using factorial hidden Markov models, and compared using the Kullback-Liebler distance, a measure of information divergence between two models. This representation is combined with an automated segmentation and clustering...
This paper proposes a novel approach for motion primitive segmentation from continuous full body human motion captured on monocular video. The proposed approach does not require a kinematic model of the person, nor any markers on the body. Instead, optical flow computed directly in the image plane is used to estimate the location of segment points. The approach is based on detecting tracking features...
This paper describes a novel approach for incremental learning of human motion pattern primitives through online observation of human motion. The observed time series data stream is first stochastically segmented into potential motion primitive segments, based on the assumption that data belonging to the same motion primitive will have the same underlying distribution. The motion segments are then...
This paper proposes hybrid image stabilization for in vivo microscopy. In vivo microscopy, which deals with living tissues in a living subject, has been becoming more and more important because it will reveal lots of unknown biological mysteries. One of the fundamental difficulties in in vivo microscopy is that the observing images from the microscope are so unstable due to the subjectpsilas trembling,...
This paper develops an approach for on-line segmentation of whole body human motion patterns during human motion observation and learning. A Hidden Markov Model is used to represent the incoming data sequence, where each model state represents the probability density estimate over a window of the data. Based on the assumption that data belonging to the same motion primitive will have the same underlying...
This paper proposes a method to recover missing data during observation by factorial hidden Markov models (FHMMs). The fundamental idea of the proposed method originates from the mimesis model, inspired by the mirror neuron system. By combining the motion recognition from partial observation algorithm and the proto-symbol based duplication of observed motion algorithm, whole body motion imitation...
This paper describes a novel approach for incremental learning of human motion pattern primitives through on-line observation of human motion. The observed motion time series data stream is first stochastically segmented into potential motion primitive segments, based on the assumption that data belonging to the same motion primitive will have the same underlying distribution. The motion segments...
This paper proposes a 3D motion recovery method from monocular images by statistical inference. The fundamental idea of the paper originates from the mimesis model, inspired by the mirror neuron system. The mimesis model is extended to include motion understanding from monocular image sequences and to imitate whole-body motion patterns in 3D space. In order to achieve this goal, (1) conversion of...
This paper describes a novel algorithm for autonomous and incremental learning of motion pattern primitives by observation of human motion. Human motion patterns are abstracted into a hidden Markov model representation, which can be used for both subsequent motion recognition and generation, analogous to the mirror neuron hypothesis in primates. As new motion patterns are observed, they are incrementally...
Motion capture systems are used to obtain motion data such that humanoid robots or computer graphics (CG) characters can behave naturally. However, it has proven to be hard not only to modify the capture data without losing its reality but also to search for the required capture data in a lot of capture data. In this paper, we provide a solution to these problems based on our previous work on symbolization...
Humanoid developments express the need for intelligent learning systems that can automatically realize behavior acquisition and symbol emergence. In the framework of mimesis model, we present an unsupervised dynamic HMM-based algorithm in order to analyze vectorial motion data. The efficiency of this algorithm is demonstrated by segmenting continuous sequence of real movements. We also propose to...
Mimesis is a hypothesis that human intelligence originated where motion recognition and motion generation interact through imitation. We previously proposed the mathematical model of mimesis using hidden Markov models (HMM) and constructed the proto symbol space from parameters of each HMM. The proto symbol space included only 10 motion patterns. No attention was paid on the relationship between behavior...
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