Affective effects of agent proximity in conversational systems

Partala, Timo; Surakka, Veikko; Lahti, Jussi

doi:10.1145/1028014.1028070

Cited by 18 publications

(24 citation statements)

References 5 publications

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“…In the proposed system, the automated search would be conducted at an altitude that would result in an average human head occupying only 1-3 pixels of the search camera's FOV, eliminating any shape information [9]. For these reasons we have employed a point detection technique from the area of Mathematical Morphology.…”

Section: Point Target Detection Phasementioning

confidence: 99%

“…To see this, suppose the FOV of the camera in the direction of the aircraft x-axis is 60°. This makes each pixel equivalent to 0.226m (average human head diameter [9]). Thus, for a target to move at least two pixels, relative to the ground, between consecutive frames it would have to be travelling at a minimum speed of approximately 25km/h -a speed that is highly improbable for the type of targets of interest.…”

Section: Temporal Tracking Phasementioning

confidence: 99%

“…The greyscale readings given by the front-end are mapped to an empirically defined normal cumulative distribution curve to enhance the point-like interpretation of the front-end filter as valid dim point-like targets can be somewhat disadvantaged in the typical filtering process. In order to populate the emission probabilities matrix, B , the colour space likelihoods (derived from their corresponding PDF curves) are fused along with the remapped version of the front-end output image within the multi-dimensional feature space of the HMM, as shown in (9). By assuming independence between feature spaces fusion of the difference information sources can be achieved in the manner stated above.…”

Section: Colour Fusionmentioning

confidence: 99%

See 2 more Smart Citations

Improved maritime target tracker using colour fusion

Westall

O’Shea

Ford

et al. 2009

2009 International Conference on High Performance Computing &Amp; Simulation

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Section: Point Target Detection Phasementioning

confidence: 99%

Section: Temporal Tracking Phasementioning

confidence: 99%

Section: Colour Fusionmentioning

confidence: 99%

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Improved maritime target tracker using colour fusion

Westall

O’Shea

Ford

et al. 2009

2009 International Conference on High Performance Computing &Amp; Simulation

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show abstract

“…In the proposed system, the automated search would be conducted at an altitude that would result in an average human head [8] occupying only 1-3 pixels of the search camera's FOV, eliminating any shape information. For these reasons we investigate point detection techniques.…”

Section: Point Target Detection Phasementioning

confidence: 99%

Evaluation of Maritime Vision Techniques for Aerial Search of Humans in Maritime Environments

Westall

Ford

O’Shea

et al. 2008

2008 Digital Image Computing: Techniques and Applications

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Searching for humans lost in vast stretches of ocean has always been a difficult task. In this paper, a range of machine vision approaches are investigated as candidate tools to mitigate the risk of human fatigue and complacency after long hours performing these kind of search tasks. Our two-phased approach utilises point target detection followed by temporal tracking of these targets. Four different point target detection techniques and two tracking techniques are evaluated. We also evaluate the use of different colour spaces for target detection. This paper has a particular focus on Hidden Markov Model based tracking techniques, which seem best able to incorporate a priori knowledge about the maritime search problem, to improve detection performance.

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“…Another proximity-based game is MirrorSpace by Roussel and colleagues where the display of information is affected by the distance of the user to the screen [20]. Partala and colleagues studied the influence of an agent's proximity on the affective response of the user [14]. Whereas valence and arousal seemed not to be influenced by proximity variances, a significant effect was found for the dominance dimension.…”

Section: Introductionmentioning

confidence: 99%

Lets Come Together – Social Navigation Behaviors of Virtual and Real Humans

Rehm¹,

André²,

Nischt³

2005

Lecture Notes in Computer Science

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Abstract. In this paper, we present a game-like scenario that is based on a model of social group dynamics inspired by theories from the social sciences. The model is augmented by a model of proxemics that simulates the role of distance and spatial orientation in human-human communication. By means of proxemics, a group of human participants may signal other humans whether they welcome new group members to join or not. In this paper, we describe the results of an experiment we conducted to shed light on the question of how humans respond to such cues when shown by virtual humans.

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Affective effects of agent proximity in conversational systems

Cited by 18 publications

References 5 publications

Improved maritime target tracker using colour fusion

Improved maritime target tracker using colour fusion

Evaluation of Maritime Vision Techniques for Aerial Search of Humans in Maritime Environments

Lets Come Together – Social Navigation Behaviors of Virtual and Real Humans

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