Fast parabola detection using estimation of distribution algorithms

Jose De Jesus Guerrero-Turrubiates, Ivan Cruz-Aceves, Sergio Ledesma, Juan Manuel Sierra-Hernandez, Jonas Velasco, Juan Gabriel Avina-Cervantes, Maria Susana Avila-Garcia, Horacio Rostro-Gonzalez, Roberto Rojas-Laguna

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Resum

This paper presents a new method based on Estimation of Distribution Algorithms (EDAs) to detect parabolic shapes in synthetic and medical images. The method computes a virtual parabola using three random boundary pixels to calculate the constant values of the generic parabola equation. The resulting parabola is evaluated by matching it with the parabolic shape in the input image by using the Hadamard product as fitness function. This proposed method is evaluated in terms of computational time and compared with two implementations of the generalized Hough transform and RANSAC method for parabola detection. Experimental results show that the proposed method outperforms the comparative methods in terms of execution time about 93.61% on synthetic images and 89% on retinal fundus and human plantar arch images. In addition, experimental results have also shown that the proposed method can be highly suitable for different medical applications.

Idioma originalAnglès
Número d’article6494390
RevistaComputational and Mathematical Methods in Medicine
Volum2017
DOIs
Estat de la publicacióPublicada - 2017
Publicat externament

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