MEDVIR: 3D visual interface applied to gene profile analisys.

The origins for this work arise in response to the increasing need for biologists and doctors to obtain tools for visual analysis of data. When dealing with multidimensional data, such as medical data, the traditional data mining techniques can be a tedious and complex task, even to some medical experts. Therefore, it is necessary to develop useful visualization techniques that can complement the expert’s criterion, and at the same time visually stimulate and make easier the process of obtaining knowledge from a dataset. Thus, the process of interpretation and understanding of the data can be greatly enriched. Multidimensionality is inherent to any medical data, requiring a time-consuming effort to get a clinical useful outcome. Unfortunately, both clinicians and biologists are not trained in managing more than four dimensions. Specifically, we were aimed to design a 3D visual interface for gene profile analysis easy in order to be used both by medical and biologist experts. In this way, a new analysis method is proposed: MedVir. This is a simple and intuitive analysis mechanism based on the visualization of any multidimensional medical data in a three dimensional space that allows interaction with experts in order to collaborate and enrich this representation. In other words, MedVir makes a powerful reduction in data dimensionality in order to represent the original information into a three dimensional environment. The experts can interact with the data and draw conclusions in a visual and quickly way.

An approach to a model of the visual cortex: disorders of vision

One of the most challenging problems that must be solved by any theoretical model purporting to explain the competence of the human brain for relational tasks is the one related with the analysis and representation of the internal structure in an extended spatial layout of múltiple objects. In this way, some of the problems are related with specific aims as how can we extract and represent spatial relationships among objects, how can we represent the movement of a selected object and so on. The main objective of this paper is the study of some plausible brain structures that can provide answers in these problems. Moreover, in order to achieve a more concrete knowledge, our study will be focused on the response of the retinal layers for optical information processing and how this information can be processed in the first cortex layers. The model to be reported is just a first trial and some major additions are needed to complete the whole vision process.

A visual framework to accelerate knowledge discovery based on dimensionality reduction minimizing degradation of quality

Tradicionalmente, el uso de técnicas de análisis de datos ha sido una de las principales vías para el descubrimiento de conocimiento oculto en grandes cantidades de datos, recopilados por expertos en diferentes dominios. Por otra parte, las técnicas de visualización también se han usado para mejorar y facilitar este proceso. Sin embargo, existen limitaciones serias en la obtención de conocimiento, ya que suele ser un proceso lento, tedioso y en muchas ocasiones infructífero, debido a la dificultad de las personas para comprender conjuntos de datos de grandes dimensiones. Otro gran inconveniente, pocas veces tenido en cuenta por los expertos que analizan grandes conjuntos de datos, es la degradación involuntaria a la que someten a los datos durante las tareas de análisis, previas a la obtención final de conclusiones. Por degradación quiere decirse que los datos pueden perder sus propiedades originales, y suele producirse por una reducción inapropiada de los datos, alterando así su naturaleza original y llevando en muchos casos a interpretaciones y conclusiones erróneas que podrían tener serias implicaciones. Además, este hecho adquiere una importancia trascendental cuando los datos pertenecen al dominio médico o biológico, y la vida de diferentes personas depende de esta toma final de decisiones, en algunas ocasiones llevada a cabo de forma inapropiada. Ésta es la motivación de la presente tesis, la cual propone un nuevo framework visual, llamado MedVir, que combina la potencia de técnicas avanzadas de visualización y minería de datos para tratar de dar solución a estos grandes inconvenientes existentes en el proceso de descubrimiento de información válida. El objetivo principal es hacer más fácil, comprensible, intuitivo y rápido el proceso de adquisición de conocimiento al que se enfrentan los expertos cuando trabajan con grandes conjuntos de datos en diferentes dominios. Para ello, en primer lugar, se lleva a cabo una fuerte disminución en el tamaño de los datos con el objetivo de facilitar al experto su manejo, y a la vez preservando intactas, en la medida de lo posible, sus propiedades originales. Después, se hace uso de efectivas técnicas de visualización para representar los datos obtenidos, permitiendo al experto interactuar de forma sencilla e intuitiva con los datos, llevar a cabo diferentes tareas de análisis de datos y así estimular visualmente su capacidad de comprensión. De este modo, el objetivo subyacente se basa en abstraer al experto, en la medida de lo posible, de la complejidad de sus datos originales para presentarle una versión más comprensible, que facilite y acelere la tarea final de descubrimiento de conocimiento. MedVir se ha aplicado satisfactoriamente, entre otros, al campo de la magnetoencefalografía (MEG), que consiste en la predicción en la rehabilitación de lesiones cerebrales traumáticas (Traumatic Brain Injury (TBI) rehabilitation prediction). Los resultados obtenidos demuestran la efectividad del framework a la hora de acelerar y facilitar el proceso de descubrimiento de conocimiento sobre conjuntos de datos reales. ABSTRACT Traditionally, the use of data analysis techniques has been one of the main ways of discovering knowledge hidden in large amounts of data, collected by experts in different domains. Moreover, visualization techniques have also been used to enhance and facilitate this process. However, there are serious limitations in the process of knowledge acquisition, as it is often a slow, tedious and many times fruitless process, due to the difficulty for human beings to understand large datasets. Another major drawback, rarely considered by experts that analyze large datasets, is the involuntary degradation to which they subject the data during analysis tasks, prior to obtaining the final conclusions. Degradation means that data can lose part of their original properties, and it is usually caused by improper data reduction, thereby altering their original nature and often leading to erroneous interpretations and conclusions that could have serious implications. Furthermore, this fact gains a trascendental importance when the data belong to medical or biological domain, and the lives of people depends on the final decision-making, which is sometimes conducted improperly. This is the motivation of this thesis, which proposes a new visual framework, called MedVir, which combines the power of advanced visualization techniques and data mining to try to solve these major problems existing in the process of discovery of valid information. Thus, the main objective is to facilitate and to make more understandable, intuitive and fast the process of knowledge acquisition that experts face when working with large datasets in different domains. To achieve this, first, a strong reduction in the size of the data is carried out in order to make the management of the data easier to the expert, while preserving intact, as far as possible, the original properties of the data. Then, effective visualization techniques are used to represent the obtained data, allowing the expert to interact easily and intuitively with the data, to carry out different data analysis tasks, and so visually stimulating their comprehension capacity. Therefore, the underlying objective is based on abstracting the expert, as far as possible, from the complexity of the original data to present him a more understandable version, thus facilitating and accelerating the task of knowledge discovery. MedVir has been succesfully applied to, among others, the field of magnetoencephalography (MEG), which consists in predicting the rehabilitation of Traumatic Brain Injury (TBI). The results obtained successfully demonstrate the effectiveness of the framework to accelerate and facilitate the process of knowledge discovery on real world datasets.

Desarrollo de un proceso de razonamiento visual para la valoración del patrimonio

El tema de la presente tesis es la valoración del patrimonio y en ella se considera que el patrimonio es un proceso cultural interesado en negociar, crear y recrear recuerdos, valores y significados culturales. Actualmente el patrimonio como proceso se está consolidando en la literatura científica, aunque la idea de que es una ‘cosa’ es dominante en el debate internacional y está respaldada tanto por políticas como prácticas de la UNESCO. El considerar el patrimonio como un proceso permite una mirada crítica, que subraya la significación. Es decir, supone el correlato que conlleva definir algo como ‘patrimonio’, o hacer que lo vaya siendo. Esta visión del concepto permite la posibilidad de comprender no sólo lo que se ha valorado, sino también lo que se ha olvidado y el porqué. El principal objetivo de esta investigación es explorar las características de un proceso de razonamiento visual para aplicarlo en el de valoración del patrimonio. Éste que se presenta, implica la creación de representaciones visuales y sus relaciones, además su meta no está centrada en producir un ambiente que sea indiferenciado de la realidad física. Con él se pretende ofrecer la posibilidad de comunicar la dimensión ‘poliédrica’ del patrimonio. Para que este nuevo proceso que propongo sea viable y sostenible, existe la necesidad de tener en cuenta el fin que se quiere lograr: la valoración. Es importante considerar que es un proceso en el cual las dinámicas de aprendizaje, comportamientos y exploración del patrimonio están directamente relacionadas con su valoración. Por lo tanto, hay que saber cómo se genera la valoración del patrimonio, con el fin de ser capaces de desarrollar el proceso adaptado a estas dinámicas. La hipótesis de esta tesis defiende que un proceso de razonamiento visual para la valoración del patrimonio permite que las personas involucradas en el proceso inicien un proceso de interacción con un elemento patrimonial y su imagen mental para llegar a ciertas conclusiones con respecto a su valor y significado. El trabajo describe la metodología que da lugar al proceso de razonamiento visual para el patrimonio, que ha sido concebido sobre un modelado descriptivo de procesos, donde se han caracterizado tres niveles: meta-nivel, de análisis y operacional. En el modelado del proceso los agentes, junto con el patrimonio, son los protagonistas. El enfoque propuesto no es sólo sobre el patrimonio, sino sobre la compleja relación entre las personas y el patrimonio. Los agentes humanos dan valor a los testimonios de la vida pasada y les imbuyen de significado. Por lo tanto, este enfoque de un proceso de razonamiento visual sirve para detectar los cambios en el valor del patrimonio, además de su dimensión poliédrica en términos espaciales y temporales. Además se ha propuesto una nueva tipología de patrimonio necesaria para sustentar un proceso de razonamiento visual para su valoración. Esta tipología está apoyada en la usabilidad del patrimonio y dentro de ella se encuentran los siguientes tipos de patrimonio: accesible, cautivo, contextualizado, descontextualizado, original y vicarial. El desarrollo de un proceso de razonamiento visual para el patrimonio es una propuesta innovadora porque integra el proceso para su valoración, contemplando la dimensión poliédrica del patrimonio y explotando la potencialidad del razonamiento visual. Además, los posibles usuarios del proceso propuesto van a tener interacción de manera directa con el patrimonio e indirecta con la información relativa a él, como por ejemplo, con los metadatos. Por tanto, el proceso propuesto posibilita que los posibles usuarios se impliquen activamente en la propia valoración del patrimonio. ABSTRACT The subject of this thesis is heritage valuation and it argues that heritage is a cultural process that is inherited, transmitted, and transformed by individuals who are interested in negotiating, creating and recreating memories and cultural meanings. Recently heritage as a process has seen a consolidation in the research, although the idea that heritage is a ‘thing’ is dominant in the international debate and is supported by policies and practice of UNESCO. Seeing heritage as a process enables a critical view, underscoring the significance. That is, it is the correlate involved in defining something as ‘heritage’, or converting it into heritage. This view of the concept allows the possibility to understand not only what has been valued, but also what has been forgotten and why. The main objective of this research is to explore the characteristics of a visual reasoning process in order to apply it to a heritage valuation. The goal of the process is not centered on producing an environment that is undifferentiated from physical reality. Thus, the objective of the process is to provide the ability to communicate the ‘polyhedral’ dimension of heritage. For this new process to be viable and sustainable, it is necessary to consider what is to be achieved: heritage valuation. It is important to note that it is a process in which the dynamics of learning, behavior and exploration heritage are directly related to its valuation. Therefore, we need to know how this valuation takes place in order to be able to develop a process that is adapted to these dynamic. The hypothesis of this thesis argues that a visual reasoning process for heritage valuation allows people involved in the process to initiate an interaction with a heritage and to build its mental image to reach certain conclusions regarding its value and meaning. The thesis describes the methodology that results in a visual reasoning process for heritage valuation, which has been based on a descriptive modeling process and have characterized three levels: meta, analysis and operational -level. The agents are the protagonists in the process, along with heritage. The proposed approach is not only about heritage but the complex relationship between people and heritage. Human operators give value to the testimonies of past life and imbue them with meaning. Therefore, this approach of a visual reasoning process is used to detect changes in the value of heritage and its multifaceted dimension in spatial and temporal terms. A new type of heritage required to support a visual reasoning process for heritage valuation has also been proposed. This type is supported by its usability and it covers the following types of heritage: available, captive, contextualized, decontextualized, original and vicarious. The development of a visual reasoning process for heritage valuation is innovative because it integrates the process for valuation of heritage, considering the multifaceted dimension of heritage and exploiting the potential of visual reasoning. In addition, potential users of the proposed process will have direct interaction with heritage and indirectly with the information about it, such as the metadata. Therefore, the proposed process enables potential users to be actively involved in their own heritage valuation.

Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles

Autonomous landing is a challenging and important technology for both military and civilian applications of Unmanned Aerial Vehicles (UAVs). In this paper, we present a novel online adaptive visual tracking algorithm for UAVs to land on an arbitrary field (that can be used as the helipad) autonomously at real-time frame rates of more than twenty frames per second. The integration of low-dimensional subspace representation method, online incremental learning approach and hierarchical tracking strategy allows the autolanding task to overcome the problems generated by the challenging situations such as significant appearance change, variant surrounding illumination, partial helipad occlusion, rapid pose variation, onboard mechanical vibration (no video stabilization), low computational capacity and delayed information communication between UAV and Ground Control Station (GCS). The tracking performance of this presented algorithm is evaluated with aerial images from real autolanding flights using manually- labelled ground truth database. The evaluation results show that this new algorithm is highly robust to track the helipad and accurate enough for closing the vision-based control loop.

Online Learning-based Robust Visual Tracking for Autonomous Landing of Unmanned Aerial Vehicles

Autonomous landing is a challenging and important technology for both military and civilian applications of Unmanned Aerial Vehicles (UAVs). In this paper, we present a novel online adaptive visual tracking algorithm for UAVs to land on an arbitrary field (that can be used as the helipad) autonomously at real-time frame rates of more than twenty frames per second. The integration of low-dimensional subspace representation method, online incremental learning approach and hierarchical tracking strategy allows the autolanding task to overcome the problems generated by the challenging situations such as significant appearance change, variant surrounding illumination, partial helipad occlusion, rapid pose variation, onboard mechanical vibration (no video stabilization), low computational capacity and delayed information communication between UAV and Ground Control Station (GCS). The tracking performance of this presented algorithm is evaluated with aerial images from real autolanding flights using manually- labelled ground truth database. The evaluation results show that this new algorithm is highly robust to track the helipad and accurate enough for closing the vision-based control loop.

Human-computer interaction based on visual hand-gesture recognition using volumetric spatiograms of local binary patterns

A more natural, intuitive, user-friendly, and less intrusive Human–Computer interface for controlling an application by executing hand gestures is presented. For this purpose, a robust vision-based hand-gesture recognition system has been developed, and a new database has been created to test it. The system is divided into three stages: detection, tracking, and recognition. The detection stage searches in every frame of a video sequence potential hand poses using a binary Support Vector Machine classifier and Local Binary Patterns as feature vectors. These detections are employed as input of a tracker to generate a spatio-temporal trajectory of hand poses. Finally, the recognition stage segments a spatio-temporal volume of data using the obtained trajectories, and compute a video descriptor called Volumetric Spatiograms of Local Binary Patterns (VS-LBP), which is delivered to a bank of SVM classifiers to perform the gesture recognition. The VS-LBP is a novel video descriptor that constitutes one of the most important contributions of the paper, which is able to provide much richer spatio-temporal information than other existing approaches in the state of the art with a manageable computational cost. Excellent results have been obtained outperforming other approaches of the state of the art.

Multiple feature points representation in target localization of wireless visual sensor networks

This paper discusses the target localization problem in wireless visual sensor networks. Additive noises and measurement errors will affect the accuracy of target localization when the visual nodes are equipped with low-resolution cameras. In the goal of improving the accuracy of target localization without prior knowledge of the target, each node extracts multiple feature points from images to represent the target at the sensor node level. A statistical method is presented to match the most correlated feature point pair for merging the position information of different sensor nodes at the base station. Besides, in the case that more than one target exists in the field of interest, a scheme for locating multiple targets is provided. Simulation results show that, our proposed method has desirable performance in improving the accuracy of locating single target or multiple targets. Results also show that the proposed method has a better trade-off between camera node usage and localization accuracy.