@ARTICLE{4760148,
author={M. Ozuysal and M. Calonder and V. Lepetit and P. Fua},
journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
title={Fast Keypoint Recognition Using Random Ferns},
year={2010},
volume={32},
number={3},
pages={448-461},
abstract={While feature point recognition is a key component of modern approaches to object detection, existing approaches require computationally expensive patch preprocessing to handle perspective distortion. In this paper, we show that formulating the problem in a naive Bayesian classification framework makes such preprocessing unnecessary and produces an algorithm that is simple, efficient, and robust. Furthermore, it scales well as the number of classes grows. To recognize the patches surrounding keypoints, our classifier uses hundreds of simple binary features and models class posterior probabilities. We make the problem computationally tractable by assuming independence between arbitrary sets of features. Even though this is not strictly true, we demonstrate that our classifier nevertheless performs remarkably well on image data sets containing very significant perspective changes.},
keywords={Bayes methods;computer vision;image classification;object detection;object recognition;probability;classifier;fast keypoint recognition;feature point recognition;image data sets;naive Bayesian classification framework;object detection;posterior probabilities;random ferns;Image processing and computer vision;feature matching;image registration;naive Bayesian.;object recognition;tracking},
doi={10.1109/TPAMI.2009.23},
ISSN={0162-8828},
month={March},}