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
fDate :
9/1/1994 12:00:00 AM
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;
Journal_Title :
Pattern Analysis and Machine Intelligence, IEEE Transactions on
