RT Journal Article
T1 Foreground detection by competitive learning for varying input distributions
A1 López-Rubio, Ezequiel
A1 Molina-Cabello, Miguel Ángel
A1 Luque-Baena, Rafael Marcos
A1 Domínguez-Merino, Enrique
K1 Visión por ordenador
K1 Reconocimiento de formas (Informática)
AB One of the most important challenges in computer vision applications is the background modeling, especially when the background is dynamic and the input distribution might not be stationary, i.e. the distribution of the input data could change with time (e.g. changing illuminations, waving trees, water, etc.). In this work, an unsupervised learning neural network is proposed which is able to cope with progressive changes in the input distribution. It is based on a dual learning mechanism which manages the changes of the input distribution separately from the cluster detection. The proposal is adequate for scenes where the background varies slowly. The performance of the method is tested against several state-of-the-art foreground detectors both quantitatively and qualitatively, with favorable results.
PB World Scientific Publishing
YR 2018
FD 2018
LK https://hdl.handle.net/10630/29690
UL https://hdl.handle.net/10630/29690
LA eng
NO López-Rubio E, Molina-Cabello MA, Luque-Baena RM, Domínguez E. Foreground Detection by Competitive Learning for Varying Input Distributions. Int J Neural Syst. 2018 Jun;28(5):1750056. doi: 10.1142/S0129065717500563.
NO Copyright Owner. Versión definitiva disponible en el DOI indicado.López-Rubio, E., Molina-Cabello, M. A., Luque-Baena, R. M., & Domínguez, E. (2018). Foreground detection by competitive learning for varying input distributions. International journal of neural systems, 28(05), 1750056.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 4 mar 2026