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Trajectory Deformations from Physical Human-Robot Interaction
Robots are finding new applications where physical interaction with a hu...
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Deep learning for scene recognition from visual data: a survey
The use of deep learning techniques has exploded during the last few yea...
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Explainable Representations of the Social State: A Model for Social Human-Robot Interactions
In this paper, we propose a minimum set of concepts and signals needed t...
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Theory of Robot Communication: II. Befriending a Robot over Time
In building on theories of Computer-Mediated Communication (CMC), Human-...
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Enhancing Energy Minimization Framework for Scene Text Recognition with Top-Down Cues
Recognizing scene text is a challenging problem, even more so than the r...
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On the Robustness of Speech Emotion Recognition for Human-Robot Interaction with Deep Neural Networks
Speech emotion recognition (SER) is an important aspect of effective hum...
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Scene recognition based on DNN and game theory with its applications in human-robot interaction
Scene recognition model based on the DNN and game theory with its applications in human-robot interaction is proposed in this paper. The use of deep learning methods in the field of image scene recognition is still in its infancy, but has become an important trend in the future. As the innovative idea of the paper, we propose the following novelties. (1) In this paper, the discrete displacement field is used to represent deformation. The registration problem is transformed into a problem of minimum energy in random field to finalize the image pre-processing task. (2) We select neighboring homogeneous sample features and the neighboring heterogeneous sample features for the extracted sample features to build a triple and modify the traditional neural network to propose the novel DNN for scene understanding. (3) The robot control is well combined to guide the robot vision for multiple tasks. The experiment is then conducted to validate the overall performance.
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