Demo Data from VLDB 2026 / reviewers in the wild / expert
Stephen A. Underwood
dblp:04/4675
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1975
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 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
1 paper |
3D vision · 70% Image recognition and object detection · 30% |
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 |
0.0 | 1 | 1975 | Visual Learning from Multiple Views · IEEE Trans. Computers 1975 |
Computer vision › Image recognition and object detection
object recognition |
0.0 | 1 | 1975 | Visual Learning from Multiple Views · IEEE Trans. Computers 1975 |
Computer vision › 3D vision › 3d object recognition
view-based object recognition |
0.0 | 1 | 1975 | Visual Learning from Multiple Views · IEEE Trans. Computers 1975 |
Computer vision › 3D vision › object representation
3d object description |
0.0 | 1 | 1975 | Visual Learning from Multiple Views · IEEE Trans. Computers 1975 |
Methods — techniques the papers use, named apart from their topics
feature mapping · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1975 | Visual Learning from Multiple ViewsabstractAn algorithm is presented in which a computer is visually shown a sequence of views of a solid planar object as the object is rotated in space. The computer automatically forms a three-dimensional description of the object. The description consists of a deterministic description of the object's surfaces and how they are interconnected to form the object, along with a measure of each surface's shape which is invariant to three-dimensional rotation. From this self-learned model of the object, the object can later be recognized from any viewing angle. The basis of the algorithm is the ability of the program to determine in a specific visual view: "What do I see now that I have seen before?" This is accomplished by generating two sets of mappings of one object description to another object description. Stephen A. Underwood, Clarence L. Coates |
IEEE Trans. Computers | 1 |