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Human interaction based on conversation and gestures is very important to realize the natural communication. This paper proposes a conversation system composed of topic selection module, conversation control module and utterance selection module. First, we apply a Bayesian network for the topic selection, and Boltzmann selection for the control of conversation. We apply term frequency inverse document...
In this paper, we propose a robust recognition and segmentation method for daily actions with a novel multi-task sequence labeling algorithm called multi-task conditional random field (MT-CRF). Multi-Task sequence labeling is a task of assigning input sequence to sequence of multi-labels that consist of one or multiple symbols in single frame. Multi-Task sequence labeling is essential for action recognition,...
This paper presents a human like segmentation method for daily life actions, such as getting up, sitting down, walking. Unsupervised segmentation methods of many previous researches cannot always assure segmentation result that coincides with human's natural sense. While the proposed method utilizes human's teacher data of segmentation to conduct human like segmentation. We assume that latent dynamics...
In this paper, we propose a fast and robust online action recognition method. The main features of the proposed method are: 1) to select a small number of critical motion features from a very large set of motion feature templates and to release humans from task of designing critical motion features, 2) to require very small calculation cost for recognition compared to conventional methods, 3) to exploit...
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