Listar por autor "Ortiz-de-Lazcano-Lobato, Juan Miguel"
Mostrando ítems 1-14 de 14
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A novel continual learning approach for competitive neural networks
Fernández-Rodríguez, Jose David; Maza Quiroga, Rosa María; Palomo-Ferrer, Esteban José
; Ortiz-de-Lazcano-Lobato, Juan Miguel; López-Rubio, Ezequiel (2022)
Continual learning tries to address the stability-plasticity dilemma to avoid catastrophic forgetting when dealing with non-stationary distributions. Prior works focused on supervised or reinforcement learning, but few ... -
Background modeling by shifted tilings of stacked denoising autoencoders
García-González, Jorge; Ortiz-de-Lazcano-Lobato, Juan Miguel; Luque-Baena, Rafael Marcos; López-Rubio, Ezequiel (2019-06-18)
The effective processing of visual data without interruption is currently of supreme importance. For that purpose, the analysis system must adapt to events that may affect the data quality and maintain its performance level ... -
Background modeling for video sequences by stacked denoising autoencoders
García-González, Jorge; Ortiz-de-Lazcano-Lobato, Juan Miguel; Luque-Baena, Rafael Marcos; Molina-Cabello, Miguel Ángel; López-Rubio, Ezequiel (2018-11-05)
Nowadays, the analysis and extraction of relevant information in visual data flows is of paramount importance. These images sequences can last for hours, which implies that the model must adapt to all kinds of circumstances ... -
Deep Learning Neural Networks to Detect Anomalies in Video Sequences.
(UMA Editorial, 2024)El principal problema abordado en esta tesis doctoral es la detección de anomalías de primer plano en secuencias de video genéricas mediante el uso de técnicas de Aprendizaje Profundo especialmente enfocadas a ser robustas ... -
Deep learning-based anomalous object detection system powered by microcontroller for PTZ cameras
Benito-Picazo, Jesús; Domínguez-Merino, Enrique; Palomo-Ferrer, Esteban José; López-Rubio, Ezequiel; Ortiz-de-Lazcano-Lobato, Juan Miguel (IEEE, 2018)
Automatic video surveillance systems are usually designed to detect anomalous objects being present in a scene or behaving dangerously. In order to perform adequately, they must incorporate models able to achieve accurate ... -
Dynamic learning rates for continual unsupervised learning.
Fernández-Rodríguez, Jose David; Palomo-Ferrer, Esteban José; Ortiz-de-Lazcano-Lobato, Juan Miguel; Ramos-Jiménez, Gonzalo Pascual; López-Rubio, Ezequiel (IOS Press, 2023)
The dilemma between stability and plasticity is crucial in machine learning, especially when non-stationary input distributions are considered. This issue can be addressed by continual learning in order to alleviate ... -
Enhanced Perspective Generation by Consensus of NeX neural models
Pacheco dos Santos Lima Junior, Marcos Sergio; Fernández-Rodríguez, Jose David; Ortiz-de-Lazcano-Lobato, Juan Miguel; López-Rubio, Ezequiel; Domínguez-Merino, Enrique (2022-07)
Neural rendering is a relatively new field of research that aims to produce high quality perspectives of a 3D scene from a reduced set of sample images. This is done with the help of deep artificial neural networks that ... -
Foreground detection by probabilistic modeling of the features discovered by stacked denoising autoencoders in noisy video sequences
García-González, Jorge; Ortiz-de-Lazcano-Lobato, Juan Miguel; Luque-Baena, Rafael Marcos; Molina-Cabello, Miguel Ángel; López-Rubio, Ezequiel (2019-06-07)
A robust foreground detection system is presented, which is resilient to noise in video sequences. The proposed model divides each video frame in patches that are fed to a stacked denoising autoencoder, which is responsible ... -
Motion Detection by Microcontroller for Panning Cameras
Benito-Picazo, Jesús; López-Rubio, Ezequiel; Ortiz-de-Lazcano-Lobato, Juan Miguel; Domínguez-Merino, Enrique; Palomo-Ferrer, Esteban José (2017-07-24)
Motion detection is the first essential process in the extraction of information regarding moving objects. The approaches based on background difference are the most used with fixed cameras to perform motion detection, ... -
Moving object detection in noisy video sequences using deep convolutional disentangled representations.
García-González, Jorge; Luque-Baena, Rafael Marcos; Ortiz-de-Lazcano-Lobato, Juan Miguel; López-Rubio, Ezequiel (2022)
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 ... -
Road pollution estimation from vehicle tracking in surveillance videos by deep convolutional neural networks
García-González, Jorge; Molina-Cabello, Miguel Ángel; Luque-Baena, Rafael Marcos; Ortiz-de-Lazcano-Lobato, Juan Miguel; López-Rubio, Ezequiel (ELSEVIER, 2021-12)
Air quality and reduction of emissions in the transport sector are determinant factors in achieving a sustainable global climate. The monitoring of emissions in traffic routes can help to improve route planning and to ... -
Sistema de recomendación basado en redes neuronales competitivas
(2017-04-19)En la presente memoria se expone la implementación del algoritmo de un sistema de recomendación basado en el PCACL (Principal Components Analysis Competitive Learning), que es una red neuronal competitiva que realiza un ... -
Sistema de reconocimiento óptico de textos manuscritos en español
(2020-11-26)Recientemente, los avances en computación, en miniaturización de componentes electrónicos, así como la creciente accesibilidad a dispositivos móviles, ha llevado a un aumento exponencial en el número de usuarios de ... -
Vehicle Classification in Traffic Environments Using the Growing Neural Gas
Molina-Cabello, Miguel Ángel; Luque-Baena, Rafael Marcos; López-Rubio, Ezequiel; Ortiz-de-Lazcano-Lobato, Juan Miguel; Domínguez-Merino, Enrique; Muñoz Pérez, José[et al.] (Springer, 2017)
Traffic monitoring is one of the most popular applications of automated video surveillance. Classification of the vehicles into types is important in order to provide the human traffic controllers with updated information ...