Currently Hough transform and its variants are the most common methods for detecting analytic curves from a binary edge image. However, these methods do not scale well when applied to complex noisy images where correct data is very small compared to the amount of incorrect data. We propose a Genetic Algorithm in combination with the Randomized Hough Transform, along with a different scoring function, to deal with such environments. This approach is also an improvement over random search and in contrast to standard Hough transform algorithms, is not limited to simple curves like straight line or circle.
CITATION STYLE
Chakraborty, S., & Deb, K. (1998). Analytic curve detection from a noisy binary edge map using genetic algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1498 LNCS, pp. 129–138). Springer Verlag. https://doi.org/10.1007/bfb0056856
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