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Fast Parabola Detection Using Estimation of Distribution Algorithms

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 resultin...

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Detalles Bibliográficos
Autores principales: Guerrero-Turrubiates, Jose de Jesus, Cruz-Aceves, Ivan, Ledesma, Sergio, Sierra-Hernandez, Juan Manuel, Velasco, Jonas, Avina-Cervantes, Juan Gabriel, Avila-Garcia, Maria Susana, Rostro-Gonzalez, Horacio, Rojas-Laguna, Roberto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5339634/
https://www.ncbi.nlm.nih.gov/pubmed/28321264
http://dx.doi.org/10.1155/2017/6494390
Descripción
Sumario: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.