4 research outputs found
Arquitectura de percepci贸n bioinspirada basada en atenci贸n para un robot social
La atenci贸n desempe帽a un papel fundamental, tanto para los seres humanos como para
los sistemas artificiales, ya que es una habilidad crucial que nos permite interactuar de manera
efectiva con nuestro entorno. Desde la infancia hasta la edad adulta, la atenci贸n nos ayuda a
concentrarnos en est铆mulos relevantes, procesar informaci贸n de manera eficiente y responder a
est铆mulos emocionales y sociales. Adem谩s, de influir en aspectos importantes de nuestras vidas,
como el aprendizaje y las interacciones sociales.
La implementaci贸n de mecanismos de atenci贸n en sistemas artificiales tiene como objetivo
aprovechar los beneficios de esta habilidad fundamental. Esto se traduce en una mejora en el
procesamiento de informaci贸n, la toma de decisiones y la interacci贸n con el entorno. La atenci贸n
en sistemas artificiales es un 谩rea de investigaci贸n en constante desarrollo, con el prop贸sito
de mejorar la capacidad de los sistemas inteligentes en diversas aplicaciones. Uno de los campos
donde m谩s se ha estudiado el concepto de la atenci贸n es en visi贸n artificial, en la cual se utiliza
para resaltar regiones relevantes en las im谩genes, lo que mejora el an谩lisis y el reconocimiento
de objetos, mientras que en la rob贸tica, la atenci贸n permite a los robots enfocarse en objetos o
eventos espec铆ficos, mejorando su capacidad de reacci贸n y ejecuci贸n de tareas.
Por este motivo, en este trabajo se propone un sistema de percepci贸n bioinspirado basado
en atenci贸n dise帽ado para mejorar la interacci贸n humano-robot. Este sistema est谩 dise帽ado para
localizar el foco de atenci贸n del robot en cada momento teniendo en cuenta la tarea actual,
los est铆mulos disponibles y el estado interno del robot. El sistema integra fen贸menos bioinspirados
como la inhibici贸n al retorno, la relocalizaci贸n del foco de atenci贸n dependiendo de los
est铆mulos, los conceptos de atenci贸n sostenida y puntual para el cambio en el foco de atenci贸n
y de agregaci贸n de est铆mulos de forma ex贸gena y end贸gena de forma independiente. Adem谩s, se
ha integrado en una plataforma rob贸tica y se ha validado su funcionamiento en diferentes aplicaciones.
Este trabajo se ha abordado desde dos perspectivas: la ampliaci贸n de las capacidades
perceptuales del robot y la mejora de la interacci贸n gracias a la integraci贸n de la atenci贸n en la
arquitectura software de las plataformas rob贸ticas. Para ello, en este trabajo se han investigado
los est铆mulos m谩s relevantes para la atenci贸n en humanos y su integraci贸n en el 谩mbito de la
rob贸tica y como realizar la agregaci贸n y fusi贸n multisensorial de estos desde un punto de vista
basado en la atenci贸n, consiguiendo una representaci贸n del entorno y seleccionando la posici贸n
del foco de atenci贸n en cada momento. Por otro lado, se ha investigado la relevancia de la
integraci贸n de este sistema artificial a una plataforma rob贸tica en lo que respecta a la interacci贸n
humano-robot, lo que ha dado lugar a un estudio que explora esta idea.Attention plays a fundamental role for both humans and artificial systems, as it is a crucial
skill that enables us to interact effectively with our environment. From childhood to adulthood,
attention helps us to focus on relevant stimuli, process information efficiently, and respond to
emotional and social stimuli. It also influences important aspects of our lives, such as learning
and social interactions.
The implementation of attention mechanisms in artificial systems aims to take advantage of
the benefits of this fundamental ability. This translates into improved information processing,
decision making and interaction with the environment. Attention in artificial systems is an area
of research in constant development, with the purpose of improving the capacity of intelligent
systems in various applications. The fields where the concept of attention has been most studied
are computer vision and robotics. In computer vision, attention is used to highlight relevant
areas in images, which improves object analysis and recognition, while in robotics, attention
allows robots to focus on specific objects or events, improving their ability to react and perform
tasks.
For this reason, this work proposes a bio-inspired attention-based perception system designed
to improve human-robot interaction. This system is designed to locate the focus of attention
of the robot at each moment, taking into account the current task, the available stimuli and
the internal state of the robot.Moreover, the architecture integrates bioinspired concepts such
as return inhibition, stimulus-dependent relocation of the focus of attention, the concepts of
sustained and punctual attention for the shift in the focus of attention and the aggregation of
exogenous and endogenous stimuli independently are integrated. In addition to this, it has been
integrated into a robotic platform, and its performance has been validated in different applications.
This work has been approached from two perspectives: the increase of the perceptual
capabilities of the robot and the improvement of the interaction thanks to the integration of
attention in the software architecture of robotic platforms. To this end, in this work, we have
investigated the most relevant stimuli for attention in humans and their integration in the robotics
environment, and how to perform the aggregation and multisensory fusion of these from
an attention-based point of view, achieving a representation of the environment and selecting
the position of the focus of attention at each moment. On the other hand, we have investigated
the relevance of the integration of this artificial system to a robotic platform in terms of
human-robot interaction, leading to a study that explores this idea.Programa de Doctorado en Ingenier铆a El茅ctrica, Electr贸nica y Autom谩tica por la Universidad Carlos III de MadridPresidente: Antonio Fern谩ndez Caballero.- Secretario: Concepci贸n Alicia Monje Micharet.- Vocal: Plinio Moreno L贸pe
Advanced Biometrics with Deep Learning
Biometrics, such as fingerprint, iris, face, hand print, hand vein, speech and gait recognition, etc., as a means of identity management have become commonplace nowadays for various applications. Biometric systems follow a typical pipeline, that is composed of separate preprocessing, feature extraction and classification. Deep learning as a data-driven representation learning approach has been shown to be a promising alternative to conventional data-agnostic and handcrafted pre-processing and feature extraction for biometric systems. Furthermore, deep learning offers an end-to-end learning paradigm to unify preprocessing, feature extraction, and recognition, based solely on biometric data. This Special Issue has collected 12 high-quality, state-of-the-art research papers that deal with challenging issues in advanced biometric systems based on deep learning. The 12 papers can be divided into 4 categories according to biometric modality; namely, face biometrics, medical electronic signals (EEG and ECG), voice print, and others
Non-cooperative iris recognition
The dramatic growth in practical applications for iris biometrics has been accompanied
by relevant developments in the underlying algorithms and techniques. Along
with the research focused on near-infrared images captured with subject cooperation,
e orts are being made to minimize the trade-o between the quality of the captured
data and the recognition accuracy on less constrained environments, where images are
obtained at the visible wavelength, at increased distances, over simpli ed acquisition
protocols and adverse lightning conditions. At a rst stage, interpolation e ects on
normalization process are addressed, pointing the outcomes in the overall recognition
error rates. Secondly, a couple of post-processing steps to the Daugman's approach
are performed, attempting to increase its performance in the particular unconstrained
environments this thesis assumes. Analysis on both frequency and spatial domains
and nally pattern recognition methods are applied in such e orts. This thesis embodies
the study on how subject recognition can be achieved, without his cooperation,
making use of iris data captured at-a-distance, on-the-move and at visible wavelength
conditions. Widely used methods designed for constrained scenarios are analyzed.Funda莽茫o para a Ci锚ncia e a Tecnologia (FCT