DocumentCode
1143771
Title
Geometric primitive extraction using a genetic algorithm
Author
Roth, Gerhard ; Levine, Martin D.
Author_Institution
Inst. for Inf. Technol., Nat. Res. Council of Canada, Ottawa, Ont., Canada
Volume
16
Issue
9
fYear
1994
fDate
9/1/1994 12:00:00 AM
Firstpage
901
Lastpage
905
Abstract
Extracting geometric primitives from geometric sensor data is an important problem in model-based vision. A minimal subset is the smallest number of points necessary to define a unique instance of a geometric primitive. A genetic algorithm based on a minimal subset representation is used to perform primitive extraction. It is shown that the genetic approach is an improvement over random search and is capable of extracting more complex primitives than the Hough transform
Keywords
computer vision; feature extraction; genetic algorithms; geometry; optimisation; Hough transform; genetic algorithm; geometric primitive extraction; geometric sensor data; minimal subset; model-based vision; random search; Computer vision; Cost function; Data mining; Genetic algorithms; Information technology; Optimization methods; Robustness; Solid modeling; Statistics; Surface fitting;
fLanguage
English
Journal_Title
Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publisher
ieee
ISSN
0162-8828
Type
jour
DOI
10.1109/34.310686
Filename
310686
Link To Document