2026-05-31T06:47:15Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/290172026-02-03T11:17:57Zcom_10630_2254col_10630_37953

López-Rubio, Ezequiel Thurnhofer-Hemsi, Karl Blázquez-Parra, Elidia Beatriz De-Cózar-Macías, Óscar Ladrón-de-Guevara-Muñoz, María del Carmen 2024-01-23T10:19:11Z 2024-01-23T10:19:11Z 2018-07-18 Ezequiel López-Rubio, Karl Thurnhofer-Hemsi, Elidia Beatriz Blázquez-Parra, Óscar David de Cózar-Macías, M. Carmen Ladrón-de-Guevara-Muñoz, A fast robust geometric fitting method for parabolic curves, Pattern Recognition, Volume 84, 2018, Pages 301-316 0031-3203 https://hdl.handle.net/10630/29017 10.1016/j.patcog.2018.07.019 Fitting discrete data obtained by image acquisition devices to a curve is a common task in many fields of science and engineering. In particular, the parabola is some of the most employed shape features in electrical engineering and telecommunication applications. Standard curve fitting techniques to solve this problem involve the minimization of squared errors. However, most of these procedures are sensitive to noise. Here, we propose an algorithm based on the minimization of absolute errors accompanied by a normalization of the directrix vector that leads to an improved stability of the method. This way, our proposal is substantially resilient to noisy samples in the input dataset. Experimental results demonstrate the good performance of the algorithm in terms of speed and accuracy when compared to previous approaches, both for synthetic and real data. spa http://creativecommons.org/licenses/by-nc-nd/4.0/ open access Attribution-NonCommercial-NoDerivatives 4.0 Internacional Ajuste de curvas Geometría analítica A fast robust geometric fitting method for parabolic curves. journal article