Vertical Parallax from Moving Shadows
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Camera calibration and light source orientation from solar shadows
Computer Vision and Image Understanding
Learning and Removing Cast Shadows through a Multidistribution Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Handbook of Knowledge Representation
Handbook of Knowledge Representation
From images to bodies: modelling and exploiting spatial occlusion and motion parallax
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
Knowledge-based adaptive thresholding from shadows
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Reasoning about shadows in a mobile robot environment
Applied Intelligence
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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.