A genetic algorithm for detecting target geometric figures.
A genetic algorithm is proposed to detect target geometric figures in an given image. Float-point encoding was adopted to process the parameters of a geometric figures to be detected. On the basis of classical Hough transform, a fitness function was obtained for each individual task, and the individuals with the highest fitness function were identified and copied into the cohort of the next generation. For the rest of the individuals, operation with multi-point crossover or uniform mutation was performed to form new individuals in the next generation. When the termination conditions for this genetic algorithm were met, the best-fitted individual was decoded and output as the parameters of the detected geometric figures. This algorithm can eliminate noise interference with good convergence and accurate results, and may save time and storage space during relatively easily programmed computation in comparison with classical Hough transform.
Xi
Wei-wen
WW
Biomedical Engineering, First Military Medical University, Guangzhou 510515, China. xww@fimmu.edu.cn
Deng
Yan
Y
Zhou
Meng
M
D000465:Algorithms; D019295:Computational Biology; D007091:Image Processing, Computer-Assisted; D008957:Models, Genetic