Dynamical patterns of human postural responses to emotional stimuli

Erotic scenes and images of mutilated bodies are emotional stimuli that have repeatedly shown to evoke specific neurophysiological responses associated with enhanced attention and perceptual processing. Remarkably however, only a handful of studies have investigated human motor reactions to emotional activation as a direct index of physical approximation or withdrawal. Given the inconclusive results of these studies, the approach-avoidance distinction, one of the most salient concepts in human motivational research, remains a broadly exploited hypothesis that has never been empirically demonstrated. Here, we investigate postural responses elicited by discrete emotional stimuli in healthy young adults. We discover that both positive and negative affective pictures induce a significant posterior deviation from postural baseline equilibrium. Further, we find that neutral pictures also evoke posterior deviation, although with a less pronounced amplitude. Exploring the dynamical evolution of postural responses to emotional pictures at high temporal resolution, we uncover a characteristic profile that remains stable for stimuli from all three affective categories. In contrast, the postural response amplitude is modulated by the emotional content of the stimulus. Our observations do not support the interpretation of postural responses to affective picture-viewing as approach-avoidance behavior. Instead, our findings indicate the involvement of a previously unrecognized motor component of the physiological mechanism underlying human orienting responses

Parametric region-based foreround segmentation in planar and multi-view sequences

Foreground segmentation in video sequences is an important area of the image processing that attracts great interest among the scientist community, since it makes possible the detection of the objects that appear in the sequences under analysis, and allows us to achieve a correct performance of high level applications which use foreground segmentation as an initial step. The current Ph.D. thesis entitled Parametric Region-Based Foreground Segmentation in Planar and Multi-View Sequences details, in the following pages, the research work carried out within this eld. In this investigation, we propose to use parametric probabilistic models at pixel-wise and region level in order to model the di erent classes that are involved in the classi cation process of the di erent regions of the image: foreground, background and, in some sequences, shadow. The development is presented in the following chapters as a generalization of the techniques proposed for objects segmentation in 2D planar sequences to 3D multi-view environment, where we establish a cooperative relationship between all the sensors that are recording the scene. Hence, di erent scenarios have been analyzed in this thesis in order to improve the foreground segmentation techniques: In the first part of this research, we present segmentation methods appropriate for 2D planar scenarios. We start dealing with foreground segmentation in static camera sequences, where a system that combines pixel-wise background model with region-based foreground and shadow models is proposed in a Bayesian classi cation framework. The research continues with the application of this method to moving camera scenarios, where the Bayesian framework is developed between foreground and background classes, both characterized with region-based models, in order to obtain a robust foreground segmentation for this kind of sequences. The second stage of the research is devoted to apply these 2D techniques to multi-view acquisition setups, where several cameras are recording the scene at the same time. At the beginning of this section, we propose a foreground segmentation system for sequences recorded by means of color and depth sensors, which combines di erent probabilistic models created for the background and foreground classes in each one of the views, by taking into account the reliability that each sensor presents. The investigation goes ahead by proposing foreground segregation methods for multi-view smart room scenarios. In these sections, we design two systems where foreground segmentation and 3D reconstruction are combined in order to improve the results of each process. The proposals end with the presentation of a multi-view segmentation system where a foreground probabilistic model is proposed in the 3D space to gather all the object information that appears in the views. The results presented in each one of the proposals show that the foreground segmentation and also the 3D reconstruction can be improved, in these scenarios, by using parametric probabilistic models for modeling the objects to segment, thus introducing the information of the object in a Bayesian classi cation framework.La segmentaci on de objetos de primer plano en secuencias de v deo es una importante area del procesado de imagen que despierta gran inter es por parte de la comunidad cient ca, ya que posibilita la detecci on de objetos que aparecen en las diferentes secuencias en an alisis, y permite el buen funcionamiento de aplicaciones de alto nivel que utilizan esta segmentaci on obtenida como par ametro de entrada. La presente tesis doctoral titulada Parametric Region-Based Foreground Segmentation in Planar and Multi-View Sequences detalla, en las p aginas que siguen, el trabajo de investigaci on desarrollado en este campo. En esta investigaci on se propone utilizar modelos probabil sticos param etricos a nivel de p xel y a nivel de regi on para modelar las diferentes clases que participan en la clasi caci on de las regiones de la imagen: primer plano, fondo y en seg un que secuencias, las regiones de sombra. El desarrollo se presenta en los cap tulos que siguen como una generalizaci on de t ecnicas propuestas para la segmentaci on de objetos en secuencias 2D mono-c amara, al entorno 3D multi-c amara, donde se establece la cooperaci on de los diferentes sensores que participan en la grabaci on de la escena. De esta manera, diferentes escenarios han sido estudiados con el objetivo de mejorar las t ecnicas de segmentaci on para cada uno de ellos: En la primera parte de la investigaci on, se presentan m etodos de segmentaci on para escenarios monoc amara. Concretamente, se comienza tratando la segmentaci on de primer plano para c amara est atica, donde se propone un sistema completo basado en la clasi caci on Bayesiana entre el modelo a nivel de p xel de nido para modelar el fondo, y los modelos a nivel de regi on creados para modelar los objetos de primer plano y la sombra que cada uno de ellos proyecta. La investigaci on prosigue con la aplicaci on de este m etodo a secuencias grabadas mediante c amara en movimiento, donde la clasi caci on Bayesiana se plantea entre las clases de fondo y primer plano, ambas caracterizadas con modelos a nivel de regi on, con el objetivo de obtener una segmentaci on robusta para este tipo de secuencias. La segunda parte de la investigaci on, se centra en la aplicaci on de estas t ecnicas mono-c amara a entornos multi-vista, donde varias c amaras graban conjuntamente la misma escena. Al inicio de dicho apartado, se propone una segmentaci on de primer plano en secuencias donde se combina una c amara de color con una c amara de profundidad en una clasi caci on que combina los diferentes modelos probabil sticos creados para el fondo y el primer plano en cada c amara, a partir de la fi abilidad que presenta cada sensor. La investigaci on prosigue proponiendo m etodos de segmentaci on de primer plano para entornos multi-vista en salas inteligentes. En estos apartados se diseñan dos sistemas donde la segmentaci on de primer plano y la reconstrucci on 3D se combinan para mejorar los resultados de cada uno de estos procesos. Las propuestas fi nalizan con la presentaci on de un sistema de segmentaci on multi-c amara donde se centraliza la informaci on del objeto a segmentar mediante el diseño de un modelo probabil stico 3D. Los resultados presentados en cada uno de los sistemas, demuestran que la segmentacion de primer plano y la reconstrucci on 3D pueden verse mejorados en estos escenarios mediante el uso de modelos probabilisticos param etricos para modelar los objetos a segmentar, introduciendo as la informaci on disponible del objeto en un marco de clasi caci on Bayesiano

3D objects reconstruction from frontal images: an example with guitars

This work deals with the automatic 3D reconstruction of objects from frontal RGB images. This aims at a better understanding of the reconstruction of 3D objects from RGB images and their use in immersive virtual environments. We propose a complete workflow that can be easily adapted to almost any other family of rigid objects. To explain and validate our method, we focus on guitars. First, we detect and segment the guitars present in the image using semantic segmentation methods based on convolutional neural networks. In a second step, we perform the final 3D reconstruction of the guitar by warping the rendered depth maps of a fitted 3D template in 2D image space to match the input silhouette. We validated our method by obtaining guitar reconstructions from real input images and renders of all guitar models available in the ShapeNet database. Numerical results for different object families were obtained by computing standard mesh evaluation metrics such as Intersection over Union, Chamfer Distance, and the F-score. The results of this study show that our method can automatically generate high-quality 3D object reconstructions from frontal images using various segmentation and 3D reconstruction techniques.Postprint (published version

Bayesian foreground segmentation and tracking using pixel-wise background model and region-based foreground model

In this paper we present a segmentation system for monocular video sequences with static camera that aims at foreground/ background separation and tracking. We propose to combine a simple pixel-wise model for the background with a general purpose region based model for the foreground. The background is modeled using one Gaussian per pixel, thus achieving a precise and easy to update model. The foreground is modeled using a Gaussian Mixture Model with feature vectors consisting of the spatial (x, y) and colour (r, g, b) components. The spatial components of this model are updated using the Expectation Maximization algorithm after the classification of each frame. The background model is formulated in the 5 dimensional feature space in order to be able to apply a Maximum A Posteriori framework for the classification. The classification is done using a graph cut algorithm that allows taking into account neighborhood information. The results presented in the paper show the improvement of the system in situations where the foreground objects have similar colors to those of the background.Peer ReviewedPostprint (published version

An adaptive viscosity regularization approach for the numerical solution of conservation laws: Application to finite element methods

We introduce an adaptive viscosity regularization approach for the numerical solution of systems of nonlinear conservation laws with shock waves. The approach seeks to solve a sequence of regularized problems consisting of the system of conservation laws and an additional Helmholtz equation for the artificial viscosity. We propose a homotopy continuation of the regularization parameters to minimize the amount of artificial viscosity subject to positivity-preserving and smoothness constraints on the numerical solution. The regularization methodology is combined with a mesh adaptation strategy that identifies the shock location and generates shock-aligned meshes, which allows to further reduce the amount of artificial dissipation and capture shocks with increased accuracy. We use the hybridizable discontinuous Galerkin method to numerically solve the regularized system of conservation laws and the continuous Galerkin method to solve the Helmholtz equation for the artificial viscosity. We show that the approach can produce approximate solutions that converge to the exact solution of the Burgers' equation. Finally, we demonstrate the performance of the method on inviscid transonic, supersonic, hypersonic flows in two dimensions. The approach is found to be accurate, robust and efficient, and yields very sharp yet smooth solutions in a few homotopy iterations.Comment: 42 pages, 22 figures, 4 table

Foreground segmentation and tracking based on foreground and background modeling techniques

The Project Framework is the detection and tracking of foreground objects in static and moving video sequences. The objective of a foreground segmentation and Tracking is to segment the scene in foreground objects and background and establish the temporal correspondence of the foreground objects. In this project we will focus on techniques that are based on a classification using a statistical model of the background and the foreground. For this reason, we will assume that the segmentation of the first frame is provided. Our objective will be to improve the models and define an appropriate updating of these models to reach a correct foreground-background segmentation minimizing False Negatives and False Positives. The tracking process makes the correspondence of the segmented objects with the objects being tracked from previous frames. Depending on the technique, the tracking can be clearly separated from the segmentation (when previous foreground information is not used for the segmentation) or can be implicit in the foreground segmentation (when we are using a priori information of the object)

Cambios en los flujos hídricos producidos por la industrialización de la agricultura española, como causa y efecto del cambio climático (1922-2016)

Programa de Doctorado en Historia y Estudios Humanísticos: Europa, América, Arte y LenguasLínea de Investigación: Historia y Estudios Humanísticos: Europa, América, Arte y GeografíaClave Programa: DHHCódigo Línea: 121A lo largo del periodo de estudio se produjeron grandes cambios en la agricultura española, que evolucionó de una agricultura tradicional a un modelo industrializado en pocas décadas. Dicha transición tuvo repercusiones muy importantes en la sostenibilidad de la agricultura y en particular en los flujos hídricos asociados a la producción agrícola, ya que la nueva agricultura requirió, entre otras transformaciones, de una expansión sin precedentes de la superficie irrigada (~300%) y consecuentemente de las infraestructuras de riego. Paralelamente, sucedieron otros cambios estructurales, como los cambios de ubicación espaciotemporal de los cultivos, los cambios de manejo y varietales, especialización en cultivos de mayor rentabilidad y el abandono de tierras marginales de secano. Partimos de la hipótesis de que la industrialización, aumentó las emisiones de GHG asociadas a los regadíos y operó sinérgicamente con el cambio climático, modificando los flujos hídricos de los agroecosistemas. Los objetivos principales del estudio son: (i) estimar la evolución de las emisiones de Greenhouse Gases (GHG) atribuibles a los regadíos españoles mediante un Life-Cycle Assessment; (ii) estimar la evolución de los flujos hídricos causada por el conjunto de los cambios ocurridos, incluyendo clima y cropland; (iii) aislar la repercusión del cambio climático y cuantificar su efecto en la evolución de los flujos hídricos. Para estimar la evolución de dichos flujos (modelo FAO-56), se combinaron indicadores de amplio uso en la literatura, como Crop Water Requirements (CWR), Actual Evapotranspiration (AET) y Green Water (GW) and Blue Water (BW), con un indicador de nueva creación denominado Violet Water (VW). Esta nueva métrica da cuenta del estrés hídrico y se define como la fracción de los CWR que no puede ser satisfecha por la precipitación que reciben los suelos agrícolas. Los resultados obtenidos muestran que las emisiones de GHG atribuibles a los regadíos se multiplicaron por 20 de 1900 a 2008. Este aumento fue causado por incremento de las infraestructuras de riego, las emisiones de metano que éstas producen y la energía necesaria para los sistemas de riego a presión. En cuanto a la evolución de los flujos hídricos, los CWR de los cultivos aumentó entre 1922 y 2016 en un 17.5%, el VW en un 54% y la AET en un 21.1%. Una vez separado del efecto que provocan los cambios en el cropland, el papel del cambio climático explica de media en torno a un 24% del aumento en la VW, un 8.7% de la AET y un 12% de los CWR en el año 2016. Concluimos que la industrialización de la agricultura, de un lado la ha transformado en un agente activo del calentamiento global y del cambio climático y de otro la ha expuesto a sus efectos.Universidad Pablo de Olavide de Sevilla. Departamento de Geografía, Historia y Filosofí