2013 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013) 2013
DOI: 10.1109/ivcnz.2013.6727059
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Semantic indoor maps

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Cited by 2 publications
(1 citation statement)
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“…Most probably the answer would be negative which supports the above claim that perhaps it is not the low-level features that are remembered but an encapsulated representation is saved which embeds the features into a set of semantic classes. The features when grouped together due to their belonging to a semantic class, depicting a real-world entity, not only helps to build meaningful representation but such a semantic representation can also lead to more simplistic maps [3] [4]. The semantic scene understanding approaches mostly either work with 2D images only, with no 3D information [5], [6] or retrieve 3D information from 2D images while ignoring the temporal relationship among images which is important in navigation perspective [7].…”
Section: Imentioning
confidence: 99%
“…Most probably the answer would be negative which supports the above claim that perhaps it is not the low-level features that are remembered but an encapsulated representation is saved which embeds the features into a set of semantic classes. The features when grouped together due to their belonging to a semantic class, depicting a real-world entity, not only helps to build meaningful representation but such a semantic representation can also lead to more simplistic maps [3] [4]. The semantic scene understanding approaches mostly either work with 2D images only, with no 3D information [5], [6] or retrieve 3D information from 2D images while ignoring the temporal relationship among images which is important in navigation perspective [7].…”
Section: Imentioning
confidence: 99%