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Francoise Veillon

dblp:62/5593 · also Françoise Veillon · DBLP profile ↗
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8ranked-venue papers
2as first author
0since 2021 · last 1998
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 6Graphics, computer vision, multimedia, augmented reality and games · 5 · 1 first-authorTheory of computation · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
2 papers
3D vision · 86% Segmentation and scene understanding · 14%
Computer graphics and multimedia
3 papers
Geometric modeling and processing · 91% Computational photography and imaging · 9%
Theoretical computer science
1 paper
Computational geometry · 100%

Topics — the 8 heaviest of 10, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Computational geometry › projective geometry
projective invariants
0.011996
Semi-local projective invariants for the recognition of smooth plane curves · Int. J. Comput. Vis. 1996
Computer vision › 3D vision
3d reconstruction
0.011993
Euclidean constraints for uncalibrated reconstruction · ICCV 1993
Computer vision › 3D vision
camera calibration
0.011993
Euclidean constraints for uncalibrated reconstruction · ICCV 1993
Computer vision › 3D vision › camera calibration
self-calibration
0.011993
Euclidean constraints for uncalibrated reconstruction · ICCV 1993
Computer vision › 3D vision › 3d reconstruction
uncalibrated reconstruction
0.011993
Euclidean constraints for uncalibrated reconstruction · ICCV 1993
Geometric modeling and processing
3d reconstruction
0.011993
Relative 3-D reconstruction using multiple uncalibrated images · CVPR 1993
Geometric modeling and processing › shape analysis
geometric structure extraction
0.011990
Finding Geometric and Relational Structures in an Image · ECCV 1990
Computational photography and imaging
camera calibration
0.011993
Relative 3-D reconstruction using multiple uncalibrated images · CVPR 1993

Methods — techniques the papers use, named apart from their topics

projective geometry · 0.0differential geometry · 0.0image analysis · 0.0reference points · 0.0projective reconstruction · 0.0point correspondence · 0.0global reconstruction · 0.0euclidean upgrade · 0.0
YearPublicationVenuePosition
1998 Joint Invariants of a Triplet of Coplanar Conics: Stability and Discriminating Power for Object Recognition
Long Quan, Francoise Veillon
Comput. Vis. Image Underst.2
1996 Semi-local projective invariants for the recognition of smooth plane curves
Stefan Carlsson, Roger Mohr, Theo Moons, Luce Morin, Charlie Rothwell, Marc Van Diest, Luc Van Gool, Francoise Veillon, Andrew Zisserman
Int. J. Comput. Vis.8
1995 Joint Invariants of a Triplet of Coplanar Conics: Stability and Discriminating Power for Object Recognition
Francoise Veillon, Long Quan, Peter F. Sturm
CAIP1
1993 Relative 3-D reconstruction using multiple uncalibrated images
abstract
It is shown how relative 3-D reconstruction for point correspondence of multiple images from uncalibrated cameras can be achieved through reference points. The original contributions with respect to other related works in the field are a direct global method for relative 3-D reconstruction and a geometrical method to select a correct set of reference points among all image points. Experimental results from both simulated and real image sequences are presented.>
Roger Mohr, Francoise Veillon, Long Quan
CVPR2
1993 Euclidean constraints for uncalibrated reconstruction
abstract
It is possible to recover the three-dimensional structure of a scene using images taken with uncalibrated cameras and pixel correspondences betweeen these images. But such reconstruction can only be performed up to a projective transformation of the 3-D space. Therefore, constraints have to be put on the reconstructed data to get the reconstruction in the Euclidean space. Such constraints arise from knowledge of the scene, such as the location of points, geometrical constraints on lines, etc. The kind of constraints that have to be added are discussed, and it is shown how they can be fed in a general framework. Experimental results on real data prove the feasibility, and experiments on simulated data address the accuracy of the results.>
Boubakeur Boufama, Roger Mohr, Francoise Veillon
ICCV3
1990 Symbolic image matching by simulated annealing
abstract
International audience
Laurent Hérault, Radu Horaud, Francoise Veillon, Jean-Jacques Niez
BMVC3
1990 Finding Geometric and Relational Structures in an Image
Radu Horaud, Francoise Veillon, Thomas Skordas
ECCV2
1977 Remark on "Algorithm 486: Numerical Inversion of Laplace Transform"
abstract
No abstract available.
Francoise Veillon
ACM Trans. Math. Softw.1