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Green Image
Issue No. 12 - December (2009 vol. 31)
ISSN: 0162-8828
pp: 2158-2167
Antonio Criminisi , Microsoft Research Cambridge, Cambridge
Kai Ni , Georgia Institute of Technology, Atlanta
John Winn , Microsoft Research Cambridge, Cambridge
Anitha Kannan , Microsoft Research Search Labs, Mountain View
This paper presents a novel method for location recognition, which exploits an epitomic representation to achieve both high efficiency and good generalization. A generative model based on epitomic image analysis captures the appearance and geometric structure of an environment while allowing for variations due to motion, occlusions, and non-Lambertian effects. The ability to model translation and scale invariance together with the fusion of diverse visual features yields enhanced generalization with economical training. Experiments on both existing and new labeled image databases result in recognition accuracy superior to state of the art with real-time computational performance.
Location class recognition, epitomic image analysis, panoramic stitching.
Antonio Criminisi, Kai Ni, John Winn, Anitha Kannan, "Epitomic Location Recognition", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 31, no. , pp. 2158-2167, December 2009, doi:10.1109/TPAMI.2009.165
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