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L. Garder

dblp:180/6297 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Computer vision › 3D vision
jigsaw puzzle solving
0.011964
Apictorial Jigsaw Puzzles: The Computer Solution of a Problem in Pattern Recognition · IEEE Trans. Electron. Comput. 1964
Computer vision › 3D vision
shape matching
0.011964
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
0.011964
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
YearPublicationVenuePosition
1964 Apictorial Jigsaw Puzzles: The Computer Solution of a Problem in Pattern Recognition
abstract
This 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