VLDB 2026 Research / reviewers in the wild / expert
Mark Stuff
dblp:96/7583
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2009
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 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 · 100% | |
| Computer graphics and multimedia
1 paper |
Image and video processing · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision
3d reconstruction |
0.1 | 1 | 2009 | Shape and Motion Reconstruction from 3D-to-1D Orthographically Projected Data via Object-Image Relations · IEEE Trans. Pattern Anal. Mach. Intell. 2009 |
Computer vision › 3D vision
shape and motion recovery |
0.1 | 1 | 2009 | Shape and Motion Reconstruction from 3D-to-1D Orthographically Projected Data via Object-Image Relations · IEEE Trans. Pattern Anal. Mach. Intell. 2009 |
Computer vision › 3D vision
structure from motion |
0.1 | 1 | 2009 | Shape and Motion Reconstruction from 3D-to-1D Orthographically Projected Data via Object-Image Relations · IEEE Trans. Pattern Anal. Mach. Intell. 2009 |
Image and video processing › radar imaging
inverse synthetic aperture radar imaging |
0.0 | 1 | 2009 | Shape and Motion Reconstruction from 3D-to-1D Orthographically Projected Data via Object-Image Relations · IEEE Trans. Pattern Anal. Mach. Intell. 2009 |
Methods — techniques the papers use, named apart from their topics
object-image relations · 0.2invariant-based reconstruction · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | Shape and Motion Reconstruction from 3D-to-1D Orthographically Projected Data via Object-Image RelationsabstractThis paper describes an invariant-based shape- and motion reconstruction algorithm for 3D-to-1D orthographically projected range data taken from unknown viewpoints. The algorithm exploits the object-image relation that arises in echo-based range data and represents a simplification and unification of previous work in the literature. Unlike one proposed approach, this method does not require uniqueness constraints, which makes its algorithmic form independent of the translation removal process (centroid removal, range alignment, etc.). The new algorithm, which simultaneously incorporates every projection and does not use an initialization in the optimization process, requires fewer calculations and is more straightforward than the previous approach. Additionally, the new algorithm is shown to be the natural extension of the approach developed by Tomasi and Kanade for 3D-to-2D orthographically projected data and is applied to a realistic inverse synthetic aperture radar imaging scenario, as well as experiments with varying amounts of aperture diversity and noise. Matthew Ferrara, Gregory Arnold, Mark Stuff |
IEEE Trans. Pattern Anal. Mach. Intell. | 3 |