Characterisation and adaptive learning in interactive video retrieval

El objetivo principal de esta tesis consiste en utilizar eficazmente los modelos de tópicos latentes para afrontar el problema de la recuperación automática de vídeo. Concretamente, se pretende mejorar tanto a nivel de eficiencia como a nivel de precisión el actual estado del arte en materia de los sitemas de recuperación automática de vídeo. En general, los modelos de tópicos latentes son un conjunto de herramientas estadísticas que permiten extraer los patrones generadores de una colección de datos. Tradicionalmente, este tipo de técnicas no han sido consideradas de gran utilidad para los sistemas de recuperación automática de vídeo debido a su alto coste computacional y a la propia complejidad del espacio de tópicos en el ámbito de la información visual.In this work, we are interested in the use of latent topics to overcome the current limitations in CBVR. Despite the potential of topic models to uncover the hidden structure of a collection, they have traditionally been unable to provide a competitive advantage in CBVR because of the high computational cost of their algorithms and the complexity of the latent space in the visual domain. Throughout this thesis we focus on designing new models and tools based on topic models to take advantage of the latent space in CBVR. Specifically, we have worked in four different areas within the retrieval process: vocabulary reduction, encoding, modelling and ranking, being our most important contributions related to both modelling and ranking

FloU-Net: An Optical Flow Network for Multi-modal Self-Supervised Image Registration

Image registration is an essential task in image processing, where the final objective is to geometrically align two or more images. In remote sensing, this process allows comparing, fusing or analyzing data, specially when multi-modal images are used. In addition, multi-modal image registration becomes fairly challenging when the images have a significant difference in scale and resolution, together with local small image deformations. For this purpose, this paper presents a novel optical flow-based image registration network, named the FloU-Net, which tries to further exploit inter-sensor synergies by means of deep learning. The proposed method is able to extract spatial information from resolution differences and through an U-Net backbone generate an optical flow field estimation to accurately register small local deformations of multi-modal images in a self-supervised fashion. For instance, the registration between Sentinel-2 (S2) and Sentinel-3 (S3) optical data is not trivial, as there are considerable spectral-spatial differences among their sensors. In this case, the higher spatial resolution of S2 result in S2 data being a convenient reference to spatially improve S3 products, as well as those of the forthcoming Fluorescence Explorer (FLEX) mission, since image registration is the initial requirement to obtain higher data processing level products. To validate our method, we compare the proposed FloU-Net with other state-of-the-art techniques using 21 coupled S2/S3 optical images from different locations of interest across Europe. The comparison is performed through different performance measures. Results show that proposed FloU-Net can outperform the compared methods. The code and dataset are available in https://github.com/ibanezfd/FloU-Net

High-Rankness Regularized Semi-Supervised Deep Metric Learning for Remote Sensing Imagery

Deep metric learning has recently received special attention in the field of remote sensing (RS) scene characterization, owing to its prominent capabilities for modeling distances among RS images based on their semantic information. Most of the existing deep metric learning methods exploit pairwise and triplet losses to learn the feature embeddings with the preservation of semantic-similarity, which requires the construction of image pairs and triplets based on the supervised information (e.g., class labels). However, generating such semantic annotations becomes a completely unaffordable task in large-scale RS archives, which may eventually constrain the availability of sufficient training data for this kind of models. To address this issue, we reformulate the deep metric learning scheme in a semi-supervised manner to effectively characterize RS scenes. Specifically, we aim at learning metric spaces by utilizing the supervised information from a small number of labeled RS images and exploring the potential decision boundaries for massive sets of unlabeled aerial scenes. In order to reach this goal, a joint loss function, composed of a normalized softmax loss with margin and a high-rankness regularization term, is proposed, as well as its corresponding optimization algorithm. The conducted experiments (including different state-of-the-art methods and two benchmark RS archives) validate the effectiveness of the proposed approach for RS image classification, clustering and retrieval tasks. The codes of this paper are publicly available.EC/H2020/734541/EU/TOOLS FOR MAPPING HUMAN EXPOSURE TO RISKY ENVIRONMENTAL CONDITIONS BY MEANS OF GROUND AND EARTH OBSERVATION DATA/EOXPOSUR

Prevalencia e incidencia de los trastornos por uso de alcohol, tabaco y otras drogas en estudiantes de una universidad pública venezolana

Antecedentes. Los trastornos por uso de sustancias (TUS) se pueden presentan en cualquier sociedad. En el pais y en sus instituciones educativas son escasos los estudios epidemiologicos. Esta situacion dificulta la compresion epidemiologica, su prevencion, planificacion y evaluacion.Objetivo. Determinar la prevalencia vital e incidencia del ultimo ano de TUS en estudiantes de la Universidad de Los Andes, Merida -Venezuela.Método. Estudio observacional transversal en estudiantes seleccionados aleatoriamente de tres campus universitarios. La muestra represento el 5% del universo: n=1.018 y se evaluo mediante un instrumento autoadministrado, anonimo y validado para Venezuela con denominaciones genericas/jerga y en concordancia a criterios diagnosticos de reconocida aceptacion. Todos fueron informados del caracter voluntario y de retirarse, si lo manifestasen. Se aplicaron tests bilaterales no parametricos, a>0,05.Resultados. La edad media fue de 21,76(3,5) anos con permanencia universitaria de 2,05(1,9) anos y el 61,1% eran mujeres. Las prevalencias de TUS fueron: abuso de alcohol 16,1%, por otras drogas del 1,8% y por dependencias: alcohol 4,3%, tabaco fumado 4,2% y por otras drogas 0,9%. Las incidencias en el ultimo ano por abuso: de alcohol 3,6%, otras drogas 0,3% y por dependencias: alcohol 1,4%, tabaco fumado 0,3% y otras drogas de 0,4%, El campus Merida tuvo significativamente mas estudiantes con dependencias (p>0,03, p>0,023, p>0,037) y el sexo masculino estuvo mas afectado por dependencias de alcohol (p>0,000).Conclusiones. La prevencion y vigilancia en la institucion sobre TUS deberian programarse segun los hallazgos y las diferencias detectadas en cada campus. Los resultados pueden orientar estudios toxicologicos no invasivas: cabellos. La separacion social entre sustancias licitas e ilicitas deberia obviarse para las estrategias y objetivos de prevencion integral

Deep Pyramidal Residual Networks for Spectral-Spatial Hyperspectral Image Classification

Convolutional neural networks (CNNs) exhibit good performance in image processing tasks, pointing themselves as the current state-of-the-art of deep learning methods. However, the intrinsic complexity of remotely sensed hyperspectral images still limits the performance of many CNN models. The high dimensionality of the HSI data, together with the underlying redundancy and noise, often makes the standard CNN approaches unable to generalize discriminative spectral-spatial features. Moreover, deeper CNN architectures also find challenges when additional layers are added, which hampers the network convergence and produces low classification accuracies. In order to mitigate these issues, this paper presents a new deep CNN architecture specially designed for the HSI data. Our new model pursues to improve the spectral-spatial features uncovered by the convolutional filters of the network. Specifically, the proposed residual-based approach gradually increases the feature map dimension at all convolutional layers, grouped in pyramidal bottleneck residual blocks, in order to involve more locations as the network depth increases while balancing the workload among all units, preserving the time complexity per layer. It can be seen as a pyramid, where the deeper the blocks, the more feature maps can be extracted. Therefore, the diversity of high-level spectral-spatial attributes can be gradually increased across layers to enhance the performance of the proposed network with the HSI data. Our experiments, conducted using four well-known HSI data sets and 10 different classification techniques, reveal that our newly developed HSI pyramidal residual model is able to provide competitive advantages (in terms of both classification accuracy and computational time) over the state-of-the-art HSI classification methods

El juego musical como recurso de calidad de la enseñanza

Memoria ID-0208. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2014-2015

Remote Sensing Image Superresolution Using Deep Residual Channel Attention

The current trend in remote sensing image superresolution (SR) is to use supervised deep learning models to effectively enhance the spatial resolution of airborne and satellite-based optical imagery. Nonetheless, the inherent complexity of these architectures/data often makes these methods very difficult to train. Despite these recent advances, the huge amount of network parameters that must be fine-tuned and the lack of suitable high-resolution remotely sensed imagery in actual operational scenarios still raise some important challenges that may become relevant limitations in the existent earth observation data production environments. To address these problems, we propose a new remote sensing SR approach that integrates a visual attention mechanism within a residual-based network design in order to allow the SR process to focus on those features extracted from land-cover components that require more computations to be superresolved. As a result, the network training process is significantly improved because it aims at learning the most relevant high-frequency information while the proposed architecture allows neglecting the low-frequency features extracted from spatially uninformative earth surface areas by means of several levels of skip connections. Our experimental assessment, conducted using the University of California at Merced and GaoFen-2 remote sensing image collections, three scaling factors, and eight different SR methods, demonstrates that our newly proposed approach exhibits competitive performance in the task of superresolving remotely sensed imagery

Concrete repairing by crack sealing by means of expansive grouts

Trabajo presentado al 7th Euro-American Congress on Construction Pathology, Rehabilitation Technology and Heritage Managament (REHABEND), celebrado en Cáceres (España) del 15 al 18 de mayo de 2018.Peer reviewe

Revista española de drogodependencias

Resumen tomado de la publicaciónAntecedentes: Los trastornos por uso de sustancias (TUS) se pueden presentar en cualquier sociedad. En el país y en sus instituciones educativas son escasos los estudios epidemiológicos. Esta situación dificulta la comprensión epidemiológica, su prevención, planificación y evaluación. Objetivo: Determinar la prevalencia vital e incidencia del último año de TUS en estudiantes de la Universidad de Los Andes, Mérida -Venezuela. Método: Estudio observacional transversal en estudiantes seleccionados aleatoriamente en tres campus universitarios. La muestra representó el 5 por ciento del universo: n=1.018 y se evaluó mediante un instrumento autoadministrado, anónimo y validado para Venezuela con denominaciones genéricas/jerga y en concordancia a criterios diagnósticos de reconocida aceptación. Todos fueron informados del carácter voluntario y de retirarse, si lo manifestase. Se aplicaron tests bilaterales no paramétricos, ? 0, 05. Conclusiones: La prevención y vigilancia en la institución sobre TUS deberían programarse según los hallazgos y las diferencias detectadas en cada campus. Los resultados pueden orientar estudios toxicológicos no invasivas: cabellos. La separación social entre sustancias lícitas e ilícitas debería obviarse para las estrategias y objetivos de prevención integral.ValenciaUniversidad de Valladolid. Facultad de Educación y Trabajo Social. Biblioteca; Campus Miguel Delibes. Paseo de Belén, 1; 47011 Valladolid; +34983423435; +34983423436; [email protected]