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Raymond H. Cuijpers

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Publication statistics

Pub. period:2010-2011
Pub. count:4
Number of co-authors:3

Co-authors

Number of publications with 3 favourite co-authors:

James F. Juola:4
David van der Pol:2
Elena Torta:2

Productive colleagues

Raymond H. Cuijpers's 3 most productive colleagues in number of publications:

James F. Juola:5
David van der Pol:2
Elena Torta:2

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May 27

13th Intl. Conf. on New Interfaces for Musical Expression

May 28

Graphics Interface 2013

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Boston UXPA Usability and UX Conf. 2013

May 24

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Raymond H. Cuijpers

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Publications by Raymond H. Cuijpers (bibliography)

2011

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Torta, Elena, Cuijpers, Raymond H. and Juola, James F. (2011): A model of the user's proximity for bayesian inference. In: Proceedings of the 6th International Conference on Human Robot Interaction 2011. pp. 273-274.

“Embodied nonverbal cues are fundamental for regulating human-human social interactions. The physical embodiment of robots makes it likely that they will have to exhibit appropriate nonverbal interactive behaviors. In this paper we propose a model of the user's proximity based on a superposition of quasi-Gaussian probability distributions which allows to express findings from HRI trials regarding distances and direction of approach in a human-robot interaction scenario. The way the model is formulated is suitable for well-established Bayesian filtering techniques, and thus the inference of the preferred distance and direction of approach in a human robot interaction scenario can be regarded as a state estimation problem. Results derived from simulations show the effectiveness of the inference process.”

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Pol, David van der, Cuijpers, Raymond H. and Juola, James F. (2011): Head pose estimation for a domestic robot. In: Proceedings of the 6th International Conference on Human Robot Interaction 2011. pp. 277-278.

“Gaze direction is an important communicative cue. In order to use this cue for human-robot interaction, software needs to be developed that enables the estimation of head pose. We began by designing an application that is able to make a good estimate of the head pose, and, contrary to earlier head pose estimation approaches, that works for non-optimal lighting conditions. Initial results show that our approach using multiple networks trained with differing datasets, gives a good estimate of head pose, and it works well in poor lighting conditions and with low-resolution images. We validated our head pose estimation method using a custom built database of images of human heads. The actual head poses were measured using a trakStar (Ascension Technologies) six-degrees-of-freedom sensor. The head pose estimation algorithm allows us to assess a person's focus of attention, which allows robots to react in a timely fashion to dynamic human communicative cues.”

2010

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Pol, David van der, Cuijpers, Raymond H. and Juola, James F. (2010): Head pose estimation for real-time low-resolution video. In: Proceedings of the 2010 Annual European Conference on Cognitive Ergonomics 2010. pp. 353-354.

“Gaze direction is an important communicative cue. In order to use this cue for human-robot interaction, software needs to be developed that enables the estimation of head pose. We began by designing an application that is be able to make a good estimate of the head pose, and, contrary to earlier neural network approaches, that works for non-optimal lighting conditions. Initial results show that the approach using multiple networks trained with differing datasets, gives a good estimate of head pose, and it works well in poor lighting conditions. The solution is not optimal yet. Smart selection rules taking into account different lighting conditions would enable us to select the neural networks trained with images with similar lighting conditions. This research will allow us to use head orientation cues in Human-Robot interaction with low-resolution cameras and in poor lighting conditions. The software allows the robot to give a timely reaction to the dynamical communicative cues used by humans.”

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Torta, Elena, Cuijpers, Raymond H. and Juola, James F. (2010): A Bayesian model for approaching a human. In: Proceedings of the 2010 Annual European Conference on Cognitive Ergonomics 2010. pp. 357-358.

“With the growing need for elder care, research is focusing on robotic assistance at home. Thus, robots must navigate in cluttered, domestic, indoor environments with the purpose of interacting with a person. Here we present a behaviour based navigation model enhanced with a low level decision making process that allows the robot to approach a human in such an environment. The model has been tested on simulation and the first results show the effectiveness of the Bayesian decision making process.”

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