@InProceedings{J.Chazalon2015,
author="J. Chazalon
and Mar{\c{c}}al Rusi{\~n}ol
and Jean-Marc Ogier",
title="Improving Document Matching Performance by Local Descriptor Filtering",
booktitle="6th IAPR International Workshop on Camera Based Document Analysis and Recognition CBDAR2015",
year="2015",
pages="1216--1220",
abstract="In this paper we propose an effective method aimed at reducing the amount of local descriptors to be indexed in a document matching framework. In an off-line training stage, the matching between the model document and incoming images is computed retaining the local descriptors from the model that steadily produce good matches. We have evaluated this approach by using the ICDAR2015 SmartDOC dataset containing near 25 000 images from documents to be captured by a mobile device. We have tested the performance of this filtering step by usingORB and SIFT local detectors and descriptors. The results show an important gain both in quality of the final matching as well as in time and space requirements.",
optnote="DAG; 600.077; 601.223; 600.084",
optnote="exported from refbase (http://refbase.cvc.uab.es/show.php?record=2680), last updated on Thu, 12 May 2016 15:10:47 +0200",
doi="10.1109/ICDAR.2015.7333957"
}