Qualitative robot localisation using information from cast shadows

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dc.contributor.author Fenelon, Valquiria
dc.contributor.author Dee, Hannah
dc.contributor.author Santos, Paulo E.
dc.date.accessioned 2010-11-15T14:44:33Z
dc.date.available 2010-11-15T14:44:33Z
dc.date.issued 2009
dc.identifier.citation Fenelon , V , Dee , H & Santos , P E 2009 , ' Qualitative robot localisation using information from cast shadows ' pp. 220-225 . , 10.1109/ROBOT.2009.5152199 en
dc.identifier.other PURE: 152821
dc.identifier.other dspace: 2160/5882
dc.identifier.uri http://hdl.handle.net/2160/5882
dc.description Santos, P. E., Dee, H. M and Fenelon, V. Qualitative robot localisation using information from cast shadows, IEEE International Conference on Robotics and Automation, pp. 220-225, Kobe, Japan, 2009. en
dc.description.abstract Abstract Recently, cognitive psychologists and others have turned their attention to the formerly neglected study of shadows, and the information they purvey. These studies show that the human perceptual system values information from shadows very highly, particularly in the perception of depth, even to the detriment of other cues. However with a few notable exceptions, computer vision systems have treated shadows not as signal but as noise. This paper makes a step towards redressing this imbalance by considering the formal representation of shadows. We take one particular aspect of reasoning about shadows, developing the idea that shadows carry information about a fragment of the viewpoint of the light source. We start from the observation that the region on which the shadow is cast is occluded by the caster with respect to the light source and build a qualitative theory about shadows using a region-based spatial formalism about occlusion. Using this spatial formalism and a machine vision system we are able to draw simple conclusions about domain objects and egolocation for a mobile robot. en
dc.format.extent 6 en
dc.language.iso eng
dc.relation.ispartof en
dc.title Qualitative robot localisation using information from cast shadows en
dc.type Text en
dc.type.publicationtype Conference paper en
dc.identifier.doi http://dx.doi.org/10.1109/ROBOT.2009.5152199
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Vision, Graphics and Visualisation Group en
dc.description.status Non peer reviewed en


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