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K.H.: Visually Supporting Depth Perception in Angiography Imaging
- In: Proceedings of the 6th International Symposium on Smart Graphics (SG06), 93–104
, 2006
"... Abstract. In this paper we propose interactive visualization techniques which support the spatial comprehension of angiogram images by emphasizing depth information and introducing combined depth cues. In particular, we propose a depth based color encoding, two variations of edge enhancement and the ..."
Abstract
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Cited by 3 (1 self)
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Abstract. In this paper we propose interactive visualization techniques which support the spatial comprehension of angiogram images by emphasizing depth information and introducing combined depth cues. In particular, we propose a depth based color encoding, two variations of edge enhancement and the application of a modified depth of field effect in order to enhance depth perception of complex blood vessel systems. All proposed techniques have been developed to improve the human depth perception and have been adapted with special consideration of the spatial comprehension of blood vessel structures. To evaluate the presented techniques, we have conducted a user study, in which users had to accomplish certain depth perception tasks. 1
information: a new theory
"... Image movement provides one of the most potent twodimensional cues for depth. From motion cues alone, the brain is capable of deriving a three-dimensional representation of distant objects. For many decades, theoretical and empirical investigations into this ability have interpreted these percepts a ..."
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Image movement provides one of the most potent twodimensional cues for depth. From motion cues alone, the brain is capable of deriving a three-dimensional representation of distant objects. For many decades, theoretical and empirical investigations into this ability have interpreted these percepts as faithful copies of the projected 3-D structures. Here we review empirical findings showing that perceived 3-D shape from motion is not veridical and cannot be accounted for by the current models. We present a probabilistic model based on a local analysis of optic flow. Although such a model does not guarantee a correct reconstruction of 3-D shape, it is shown to be consistent with human performance. To perceive the 3-D shape of objects from two-dimensional

