2026-05-27T21:05:37Zhttps://riuma.uma.es/rest/oai/request

oai:riuma.uma.es:10630/252862026-02-03T12:07:16Zcom_10630_2254col_10630_37959

García-González, Jorge Luque-Baena, Rafael Marcos Ortiz-de-Lazcano-Lobato, Juan Miguel López-Rubio, Ezequiel 2022-10-25T07:21:36Z 2022-10-25T07:21:36Z 2022 https://hdl.handle.net/10630/25286 Noise robustness is crucial when approaching a moving de- tection problem since image noise is easily mistaken for movement. In order to deal with the noise, deep denoising autoencoders are commonly proposed to be applied on image patches with an inherent disadvantage with respect to the segmentation resolution. In this work, a fully convolutional autoencoder-based moving detection model is proposed in order to deal with noise with no patch extraction required. Different autoencoder structures and training strategies are also tested to get insights into the best network design ap- proach. eng open access Procesado de imágenes Moving object detection in noisy video sequences using deep convolutional disentangled representations. conference output