A new evaluation framework and image dataset for keypoint extraction and feature descriptor matching
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2013-02
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Key point extraction and description mechanisms play a crucial role in image matching, where several image points must be accurately identified to robustly estimate a transformation or to recognize an object or a scene -- New procedures for keypoint extraction and for feature description are continuously emerging -- In order to assess them accurately, normalized data and evaluation protocols are required -- In response to these needs, we present a (1) new evaluation framework that allow assessing the performance of the state-of-the-art feature point extraction and description mechanisms, (2) a new image dataset acquired under controlled affine and photometric transformations and (3) a testing image generator -- Our evaluation framework allows generating detailed curves about the performance of different approaches, providing a valuable insight about their behavior -- Also, it can be easily integrated in many research and development environments -- The contributions mentioned above are available on-line for the use of the scientific community
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@inproceedings{ibarandiaran2013evaluation,
author ={Inigo Barandiaran and Camilo Cortes and Marcos Nieto and Manuel Grana and Oscar E. Ruiz},
title ={A New Evaluation Framework and Image Dataset for Key Point Extraction and Feature Descriptor Matching},
booktitle ={ {VISAPP} 2013 - Proceedings of the International Conference on ComputerVision Theory and Applications, Volume 1, Barcelona, Spain, 21-24 February, 2013 },
year ={2013},
editor ={Sebastiano Battiato and Jose Braz},
address ={Barcelona, Spain},
keys ={Keypoint Extraction, Feature Descriptor, Keypoint Matching, Homography Estimation},
organization ={INSTICC},
pages ={252--257},
volume={1},
publisher ={SCITEPRESS},
abstract ={Key point extraction and description mechanisms play a crucial role in image matching, where several image
points must be accurately identi�ed to robustly estimate a transformation or to recognize an object or a scene.
New procedures for keypoint extraction and for feature description are continuously emerging. In order to
assess them accurately, normalized data and evaluation protocols are required. In response to these needs, we
present a (1) new evaluation framework that allow assessing the performance of the state-of-the-art feature
point extraction and description mechanisms, (2) a new image dataset acquired under controlled af�ne and
photometric transformations and (3) a testing image generator. Our evaluation framework allows generating
detailed curves about the performance of different approaches, providing a valuable insight about their be-
havior. Also, it can be easily integrated in many research and development environments. The contributions
mentioned above are available on-line for the use of the scienti�c community},
isbn ={978-989-8565-47-1},
crossref = {DBLP:conf/visapp/2013-1},
timestamp = {Thu, 23 Oct 2014 22:01:45 +0200},
biburl = {http://dblp.uni-trier.de/rec/bib/conf/visapp/BarandiaranCNGR13},
bibsource = {dblp computer science bibliography, http://dblp.org}
}