@inproceedings{criminisi2008epitomic,
author = {Criminisi, Antonio and Kannan, Anitha and Ni, Kai and Winn, John},
title = {Epitomic Location Recognition},
booktitle = {Proc IEEE Conference on Computer Vision (CVPR). Winner of BEST STUDENT PAPER RUNNER UP AWARD.},
year = {2008},
month = {January},
abstract = {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 yield enhanced generalization with economical training. Experiments on both existing and new labelled image databases result in recognition accuracy superior to state of the art with real-time computational performance},
publisher = {IEEE Computer Society},
url = {http://approjects.co.za/?big=en-us/research/publication/epitomic-location-recognition/},
edition = {Proc IEEE Conference on Computer Vision (CVPR). Winner of BEST STUDENT PAPER RUNNER UP AWARD.},
note = {Winner of Best Student Paper Runner Up Award.},
}