Demo Data from EDBT 2026 / reviewers in the wild / expert
R. Hartley
dblp:30/1983
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2003
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 1
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
1 paper |
3D vision · 67% Optimization for machine learning · 33% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision › structure from motion
factorization |
0.0 | 1 | 2003 | Outlier Correction in Image Sequences for the Affine Camera · ICCV 2003 |
Machine learning › Optimization for machine learning › low-rank optimization
robust matrix factorization |
0.0 | 1 | 2003 | Outlier Correction in Image Sequences for the Affine Camera · ICCV 2003 |
Computer vision › 3D vision
structure from motion |
0.0 | 1 | 2003 | Outlier Correction in Image Sequences for the Affine Camera · ICCV 2003 |
Methods — techniques the papers use, named apart from their topics
robust estimation · 0.0SVD · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2003 | Outlier Correction in Image Sequences for the Affine CameraabstractIt is widely known that, for the affine camera model, both shape and motion can be factorized directly from the so-called image measurement matrix constructed from image point coordinates. The ability to extract both shape and motion from this matrix by a single SVD operation makes this shape-from-motion approach attractive; however, it can not deal with missing feature points and, in the presence of outliers, a direct SVD to the matrix would yield highly unreliable shape and motion components. Here, we present an outlier correction scheme that iteratively updates the elements of the image measurement matrix. The magnitude and sign of the update to each element is dependent upon the residual robustly estimated in each iteration. The result is that outliers are corrected and retained, giving improved reconstruction and smaller reprojection errors. Our iterative outlier correction scheme has been applied to both synthesized and real video sequences. The results obtained are remarkably good. Du Q. Huynh, R. Hartley, Anders Heyden |
ICCV | 2 |