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@inproceedings{bhat:inria-00486749,
author = {Bhat, Srikrishna and Berger, Marie-Odile and Simon, Gilles and Sur,
Fr{\'e}d{\'e}ric},
title = {{Transitive Closure based visual words for point matching in video
sequence}},
booktitle = {{20th International Conference on Pattern Recognition - ICPR 2010}},
year = {2010},
address = {Istanbul, Turquie},
abstract = {{We present Transitive Closure based visual word formation technique
for obtaining robust object representations from smoothly varying
multiple views. Each one of our visual words is represented by a
set of feature vectors which is obtained by performing transitive
closure operation on SIFT features. We also present range-reducing
tree structure to speed up the transitive closure operation. The
robustness of our visual word representation is demonstrated for
Structure from Motion (SfM) and location identification in video
images.}},
affiliation = {MAGRIT - INRIA Lorraine - LORIA},
audience = {internationale },
file = {icpr2010-CameraReady.pdf:http\://hal.inria.fr/inria-00486749/PDF/icpr2010-CameraReady.pdf:PDF},
hal_id = {inria-00486749},
url = {http://hal.inria.fr/inria-00486749}
}
@inproceedings{bhat:inria-00576915,
author = {Bhat, Srikrishna and Berger, Marie-Odile and Sur, Fr{\'e}d{\'e}ric},
title = {{Visual words for 3D reconstruction and pose computation}},
booktitle = {{The First Joint 3DIM/3DPVT Conference}},
year = {2011},
address = {Hangzhou, Chine},
month = mar,
abstract = {{Visual vocabularies are standard tools in the object/image classification
literature, and are emerging as a new tool for building point correspondences
for pose estimation. This paper proposes several visual word based
methods for point matching, with structure from motion and pose estimation
applications in view. The three dimensional geometry of a scene is
first extracted with bundle adjustment techniques based on the keypoint
correspondences. These correspondences are obtained by grouping the
set of all SIFT descriptors from the training images into visual
words. We obtain a more accurate 3D geometry than with classical
image-to-image point matching. In the second step, these visual words
serve as 3D point descriptors robust to viewpoint change, and are
then used for building 2D-3D correspondences for a test image, yielding
the pose of the camera by solving the PnP problem. We compare several
visual word formation techniques w.r.t robustness to viewpoint change
between the learning and test images and discuss the required computational
time.}},
affiliation = {MAGRIT - INRIA Lorraine - LORIA},
audience = {internationale },
file = {PID1714817.pdf:http\://hal.inria.fr/inria-00576915/PDF/PID1714817.pdf:PDF},
hal_id = {inria-00576915},
url = {http://hal.inria.fr/inria-00576915}
}
@inproceedings{noury:inria-00432992,
author = {Noury, Nicolas and Sur, Fr{\'e}d{\'e}ric and Berger, Marie-Odile},
title = {{Mod{\`e}le a contrario pour la mise en correspondance robuste sous
contraintes {\'e}pipolaires et photom{\'e}triques}},
booktitle = {{17i{\`e}me congr{\`e}s francophone AFRIF-AFIA, Reconnaissance des
Formes et Intelligence Artificielle - RFIA 2010}},
year = {2010},
pages = {8p},
address = {Caen, France},
month = jan,
organization = {Universit{\'e} de Caen Basse-Normandie and laboratoire GREYC},
abstract = {{La mise en correspondance de points d'int{\'e}r{\^e}t entre deux
vues est une des {\'e}tapes cl{\'e}s en vision par ordinateur, en
particulier dans l'analyse de la structure et du mouvement. Apr{\`e}s
l'extraction de points d'int{\'e}r{\^e}t, deux {\'e}tapes sont g{\'e}n{\'e}ralement
mises en oeuvre : la mise en correspondance de ceux-ci en gardant
les "meilleurs appariements" selon une mesure de ressemblance photom{\'e}trique
adapt{\'e}e, puis la s{\'e}lection des appariements coh{\'e}rents
avec la g{\'e}om{\'e}trie induite par le mouvement de la cam{\'e}ra.
La pr{\'e}sence de motifs r{\'e}p{\'e}t{\'e}s, ou des forts changements
de point de vue peuvent g{\'e}n{\'e}rer de nombreux appariements
aberrants. Nous pr{\'e}sentons une m{\'e}thode a contrario {\'e}tendant
celle de Moisan et Stival, qui regroupe ces deux {\'e}tapes. L'approche
propos{\'e}e ne n{\'e}cessite pas de param{\`e}tre critique et permet
un gain significatif en nombre d'appariements obtenus et en pr{\'e}cision,
en particulier en pr{\'e}sence de motifs r{\'e}p{\'e}t{\'e}s ou de
forts changements des points de vue.}},
affiliation = {MAGRIT - INRIA Lorraine - LORIA},
audience = {nationale },
file = {rfia2010noury\\_final.pdf:http\://hal.inria.fr/inria-00432992/PDF/rfia2010noury\\_final.pdf:PDF},
hal_id = {inria-00432992},
keywords = {Points of interest matching, repeated patterns, a contrario model.},
url = {http://hal.inria.fr/inria-00432992}
}
@inproceedings{noury:inria-00515375,
author = {Noury, Nicolas and Sur, Fr{\'e}d{\'e}ric and Berger, Marie-Odile},
title = {{How to Overcome Perceptual Aliasing in ASIFT?}},
booktitle = {{6th International Symposium on Visual Computing - ISVC 2010}},
year = {2010},
series = {Lecture Notes in Computer Science },
address = {Las Vegas, {\'E}tats-Unis},
month = sep,
publisher = {Springer},
abstract = {{SIFT is one of the most popular algorithms to extract points of interest
from images. It is a scale+rotation invariant method. As a consequence,
if one compares points of interest between two images subject to
a large viewpoint change, then only a few, if any, common points
will be retrieved. This may lead subsequent algorithms to failure,
especially when considering structure and motion or object recognition
problems. Reaching at least affine invariance is crucial for reliable
point correspondences. Successful approaches have been recently proposed
by several authors to strengthen scale+rotation invariance into affine
invariance, using viewpoint simulation (e.g. the ASIFT algorithm).
However, almost all resulting algorithms fail in presence of repeated
patterns, which are common in man-made environments, because of the
so-called perceptual aliasing. Focusing on ASIFT, we show how to
overcome the perceptual aliasing problem. To the best of our knowledge,
the resulting algorithm performs better than any existing generic
point matching procedure.}},
affiliation = {MAGRIT - INRIA Lorraine - LORIA},
audience = {internationale },
file = {NourySurBergerISVC10.pdf:http\://hal.inria.fr/inria-00515375/PDF/NourySurBergerISVC10.pdf:PDF},
hal_id = {inria-00515375},
keywords = {ASIFT; SIFT; repeated patterns; perceptual aliasing; a contrario model},
url = {http://hal.inria.fr/inria-00515375}
}
@inproceedings{noury:inria-00164805,
author = {Noury, Nicolas and Sur, Fr{\'e}d{\'e}ric and Berger, Marie-Odile},
title = {{Mod{\`e}les statistiques pour l'estimation de la matrice fondamentale}},
booktitle = {{Congr{\`e}s francophone des jeunes chercheurs en vision par ordinateur
- ORASIS'07}},
year = {2007},
pages = {8},
address = {Obernai, France},
month = jun,
abstract = {{Fundamental matrix estimation between two views is a cornerstone
of structure from motion problems. Estimation is usually achieved
in a twofold procedure : 1) identify matching points of interest
between the two views, and 2) sort out the best matches through a
robust filtering. The success of this latter step depends on the
accuracy of the former one, and on several thresholds. Setting those
thresholds is quite touchy and makes it difficult to automate the
whole process. L. Moisan et B. Stival [8] have proposed a statistical
model that enables to get rid of these thresholds. We assess over
real and synthetic data that this model performs better than existing
ones, especially from a robustness, accuracy, and computation time
point of view. Besides, following their works, we propose an integrated
algorithm that allows simultaneously to match interest points and
to estimate the fundamental matrix between two views. We show that
this algorithm is robust toward repeated patterns which are difficult
to unambiguously match.}},
affiliation = {MAGRIT - INRIA Lorraine - LORIA},
audience = {nationale },
file = {orasis07\\_final.pdf:http\://hal.inria.fr/inria-00164805/PDF/orasis07\\_final.pdf:PDF},
hal_id = {inria-00164805},
keywords = {Fundamental matrix ; probabilistic model ; repeated patterns || Matrice
fondamentale ; mod{\`e}le probabiliste ; motifs r{\'e}p{\'e}t{\'e}s},
url = {http://hal.inria.fr/inria-00164805}
}
@inproceedings{noury:inria-00164807,
author = {Noury, Nicolas and Sur, Fr{\'e}d{\'e}ric and Berger, Marie-Odile},
title = {{Fundamental matrix estimation without prior match}},
booktitle = {{14th IEEE International Conference on Image Processing - ICIP 2007}},
year = {2007},
pages = {513--516},
address = {San Antonio, Texas, {\'E}tats-Unis},
month = sep,
publisher = {IEEE},
abstract = {{This paper presents a probabilistic framework for computing correspondences
and fundamental matrix in the structure from motion problem. Inspired
by Moisan and Stival, we suggest using an a contrario model, which
is a good answer to threshold problems in the robust filtering context.
Contrary to most existing algorithms where perceptual correspondence
setting and geometry evaluation are independent steps, the proposed
algorithm is an all-in-one approach. We show that it is robust to
repeated patterns which are usually difficult to unambiguously match
and thus raise many problems in the fundamental matrix estimation.}},
affiliation = {MAGRIT - INRIA Lorraine - LORIA},
audience = {internationale },
doi = {10.1109/ICIP.2007.4379004 },
file = {icip07\\_noury\\_final.pdf:http\://hal.inria.fr/inria-00164807/PDF/icip07\\_noury\\_final.pdf:PDF},
hal_id = {inria-00164807},
keywords = {Fundamental matrix ; probabilistic model ; repeated patterns},
url = {http://hal.inria.fr/inria-00164807}
}
@article{sur:hal-00876215,
author = {Sur, Fr{\'e}d{\'e}ric and Noury, Nicolas and Berger, Marie-Odile},
title = {{An A Contrario Model for Matching Interest Points under Geometric
and Photometric Constraints}},
journal = {SIAM Journal on Imaging Sciences},
year = {2013},
volume = {6},
pages = {1956-1978},
number = {4 },
abstract = {{Finding point correspondences between two views is generally based
on the matching of local photometric descriptors. A subsequent geometric
constraint ensures that the set of matching points is consistent
with a realistic camera motion. Starting from a paper by Moisan and
Stival, we propose an a contrario model for matching interest points
based on descriptor similarity and geometric constraints. The resulting
algorithm has adaptive matching thresholds and is able to detect
point correspondences whose associated descriptors are not the first
nearest neighbor. We also discuss the specific difficulties raised
by images containing repeated patterns which are likely to introduce
correspondences beyond the nearest neighbor.}},
affiliation = {MAGRIT - INRIA Nancy - Grand Est / LORIA , MAGRIT - INRIA Lorraine
- LORIA},
audience = {internationale },
doi = {10.1137/120871766 },
file = {120871766.pdf:http\://hal.inria.fr/hal-00876215/PDF/120871766.pdf:PDF},
hal_id = {hal-00876215},
keywords = {point correspondence problem, a contrario model, generalized RANSAC,
repeated patterns},
publisher = {Society for Industrial and Applied Mathematics},
url = {http://hal.inria.fr/hal-00876215}
}
@inproceedings{sur:inria-00319704,
author = {Sur, Fr{\'e}d{\'e}ric and Noury, Nicolas and Berger, Marie-Odile},
title = {{Computing the uncertainty of the 8 point algorithm for fundamental
matrix estimation}},
booktitle = {{19th British Machine Vision Conference - BMVC 2008}},
year = {2008},
pages = {10},
address = {Leeds, Royaume-Uni},
abstract = {{Fundamental matrix estimation is difficult since it is often based
on correspondences that are spoilt by noise and outliers. Outliers
must be thrown out via robust statistics, and noise gives uncertainty.
In this article we provide a closed-form formula for the uncertainty
of the so-called 8 point algorithm, which is a basic tool for fundamental
matrix estimation via robust methods. As an application, we modify
a well established robust algorithm accordingly, leading to a new
criterion to recover point correspondences under epipolar constraint,
balanced by the uncertainty of the estimation.}},
affiliation = {MAGRIT - INRIA Lorraine - LORIA},
audience = {internationale },
file = {article.pdf:http\://hal.inria.fr/inria-00319704/PDF/article.pdf:PDF},
hal_id = {inria-00319704},
url = {http://hal.inria.fr/inria-00319704}
}
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