Demo Data from SIGIR 2026 / reviewers in the wild / expert
Francoise Veillon
dblp:62/5593 · also Françoise Veillon
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computational geometry › projective geometry
projective invariants |
0.0 | 1 | 1996 | Semi-local projective invariants for the recognition of smooth plane curves · Int. J. Comput. Vis. 1996 |
Computer vision › 3D vision
3d reconstruction |
0.0 | 1 | 1993 | Euclidean constraints for uncalibrated reconstruction · ICCV 1993 |
Computer vision › 3D vision
camera calibration |
0.0 | 1 | 1993 | Euclidean constraints for uncalibrated reconstruction · ICCV 1993 |
Computer vision › 3D vision › camera calibration
self-calibration |
0.0 | 1 | 1993 | Euclidean constraints for uncalibrated reconstruction · ICCV 1993 |
Computer vision › 3D vision › 3d reconstruction
uncalibrated reconstruction |
0.0 | 1 | 1993 | Euclidean constraints for uncalibrated reconstruction · ICCV 1993 |
Geometric modeling and processing
3d reconstruction |
0.0 | 1 | 1993 | Relative 3-D reconstruction using multiple uncalibrated images · CVPR 1993 |
Geometric modeling and processing › shape analysis
geometric structure extraction |
0.0 | 1 | 1990 | Finding Geometric and Relational Structures in an Image · ECCV 1990 |
Computational photography and imaging
camera calibration |
0.0 | 1 | 1993 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 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 |
CAIP | 1 |
| 1993 | Relative 3-D reconstruction using multiple uncalibrated imagesabstractIt 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 |
CVPR | 2 |
| 1993 | Euclidean constraints for uncalibrated reconstructionabstractIt 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 |
ICCV | 3 |
| 1990 | Symbolic image matching by simulated annealingabstractInternational audience Laurent Hérault, Radu Horaud, Francoise Veillon, Jean-Jacques Niez |
BMVC | 3 |
| 1990 | Finding Geometric and Relational Structures in an Image
Radu Horaud, Francoise Veillon, Thomas Skordas |
ECCV | 2 |
| 1977 | Remark on "Algorithm 486: Numerical Inversion of Laplace Transform"abstractNo abstract available. Francoise Veillon |
ACM Trans. Math. Softw. | 1 |