Demo Data from SIGIR 2026 / reviewers in the wild / expert
L. Garder
dblp:180/6297
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 1964
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 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 · 87% Multi-agent systems · 13% | |
| Computer graphics and multimedia
1 paper |
Geometric modeling and processing · 100% |
Topics — the 3 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Computer vision › 3D vision
jigsaw puzzle solving |
0.0 | 1 | 1964 | Apictorial Jigsaw Puzzles: The Computer Solution of a Problem in Pattern Recognition · IEEE Trans. Electron. Comput. 1964 |
Computer vision › 3D vision
shape matching |
0.0 | 1 | 1964 | Apictorial Jigsaw Puzzles: The Computer Solution of a Problem in Pattern Recognition · IEEE Trans. Electron. Comput. 1964 |
Knowledge, reasoning and agents › Multi-agent systems
game solving |
0.0 | 1 | 1964 | Apictorial Jigsaw Puzzles: The Computer Solution of a Problem in Pattern Recognition · IEEE Trans. Electron. Comput. 1964 |
Methods — techniques the papers use, named apart from their topics
contour characterization · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1964 | Apictorial Jigsaw Puzzles: The Computer Solution of a Problem in Pattern RecognitionabstractThis paper describes the development of a procedure that enables a digital computer to solve ``apictorial'' jigsaw puzzles, i.e., puzzles in which all pieces are uniformly gray and the only available information is the shape of the pieces. The problem was selected because it provided an excellent vehicle to develop computer techniques for manipulation of arbitrary geometric patterns, for pattern identification, and for game solving. The kinds of puzzles and their properties are discussed in detail. Methods are described for characterizing and classifying piece contours, for selecting and ordering pieces that are ``most likely'' to mate with a given piece, for determining likelihood of fit, for overcoming ambiguities, and for evaluation of the progressive puzzle assembly. An illustration of an actual computer solution of a puzzle is given. Herbert Freeman, L. Garder |
IEEE Trans. Electron. Comput. | 2 |