Fast robust GA-based ellipse detection

被引：22

作者：

Yao, J ^{[1
]}

Kharma, N ^{[1
]}

Grogono, P ^{[1
]}

机构：

[1] Concordia Univ, Dept Elect & Comp Engn, Montreal, PQ H3G 1M8, Canada

来源：

PROCEEDINGS OF THE 17TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION, VOL 2 | 2004年

关键词：

genetic algorithms; clustering; sharing GA; Randomized Hough Transform; shape detection; ellipse detection;

D O I：

10.1109/ICPR.2004.1334394

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper discusses a novel and effective technique for extracting multiple ellipses from an image, using a Multi-Population Genetic Algorithm (MPGA). MPGA evolves a number of subpopulations in parallel, each of which is clustered around an actual or perceived ellipse. It utilizes both evolution and clustering to direct the search for ellipses - full or partial. MPGA is explained in detail, and compared with both the widely used Randomized Hough Transform (RHT) and the Sharing Genetic Algorithm (SGA). In thorough and fair experimental tests, utilizing both synthetic and real-world images, MPGA exhibits solid advantages over RHT and SGA in terms of accuracy of recognition - even in the presence of noise or/and multiple imperfect ellipses, as well as speed of computation.

引用

页码：859 / 862

页数：4