Road Markings Segmentation from LIDAR Point Clouds using Reflectivity Information

Lane detection algorithms are crucial for the development of autonomous vehicles technologies. The more extended approach is to use cameras as sensors. However, LIDAR sensors can cope with weather and light conditions that cameras can not. In this paper, we introduce a method to extract road markings from the reflectivity data of a 64-layers LIDAR sensor. First, a plane segmentation method along with region grow clustering was used to extract the road plane. Then we applied an adaptive thresholding based on Otsu s method and finally, we fitted line models to filter out the remaining outliers. The algorithm was tested on a test track at 60km/h and a highway at 100km/h. Results showed the algorithm was reliable and precise. There was a clear improvement when using reflectivity data in comparison to the use of the raw intensity data both of them provided by the LIDAR sensor

Asking for Help with a Cost in Reinforcement Learning

Reinforcement learning (RL) is a powerful tool for developing intelligent agents, and the use of neural networks makes RL techniques more scalable to challenging real-world applications, from task-oriented dialogue systems to autonomous driving. However, one of the major bottlenecks to the adoption of RL is efficiency, as it often takes many time steps to learn an acceptable policy. To address this problem, we investigate the idea of allowing the agent to ask for advice from a teacher. We formalize this concept in a framework called ask-for-help RL, which entails augmenting a Markov decision process with a teacher-query action that can be taken at a fixed cost in any state. In this task, the agent faces a dilemma between exploration, exploitation, and teacher-querying. To make this trade-off, we propose an action selection strategy that is rooted in the classical notion of value-of-information, and suggest a practical implementation that is based on deep Q-learning. This algorithm, called VOE/Q, can jointly decide between taking a particular environment action or querying the teacher, and is sensitive to the query cost. We then perform experiments in two domains: a maze navigation task and the Atari game Freeway. When the teacher is excluded, the algorithm shows substantial gains over many other exploration strategies from the literature. With the teacher included, we again find that the algorithm outperforms baselines. By taking advantage of the teacher, higher cumulative reward can be achieved than with standard RL alone. Together, our results point to a promising approach to both RL and ask-for-help RL

Caracterización del Edema Macular Diabético mediante análisis automático de Tomografías de Coherencia Óptica

Programa Oficial de Doctorado en Computación. 5009V01[Abstract] Diabetic Macular Edema (DME) is one of the most important complications of diabetes and a leading cause of preventable blindness in the developed countries. Among the di erent image modalities, Optical Coherence Tomography (OCT) is a non-invasive, cross-sectional and high-resolution imaging technique that is commonly used for the analysis and interpretation of many retinal structures and ocular disorders. In this way, the development of Computer-Aided Diagnosis (CAD) systems has become relevant over the recent years, facilitating and simplifying the work of the clinical specialists in many relevant diagnostic processes, replacing manual procedures that are tedious and highly time-consuming. This thesis proposes a complete methodology for the identi cation and characterization of DMEs using OCT images. To do so, the system combines and exploits di erent clinical knowledge with image processing and machine learning strategies. This automatic system is able to identify and characterize the main retinal structures and several pathological conditions that are associated with the DME disease, following the clinical classi cation of reference in the ophthalmological eld. Despite the complexity and heterogeneity of this relevant ocular pathology, the proposed system achieved satisfactory results, proving to be robust enough to be used in the daily clinical practice, helping the clinicians to produce a more accurate diagnosis and indicate adequate treatments[Resumen] El Edema Macular Diabético (EMD) es una de las complicaciones más importantes de la diabetes y una de las principales causas de ceguera prevenible en los países desarrollados. Entre las diferentes modalidades de imagen, la Tomografía de Coherencia Óptica (TCO) es una técnica de imagen no invasiva, transversal y de alta resolución que se usa comúnmente para el análisis e interpretación de múltiples estructuras retinianas y trastornos oculares. De esta manera, el desarrollo de los sistemas de Diagnóstico Asistido por Ordenador (DAO) se ha vuelto relevante en los últimos años, facilitando y simplificando el trabajo de los especialistas clínicos en muchos procesos diagnósticos relevantes, reemplazando procedimientos manuales que son tediosos y requieren mucho tiempo. Esta tesis propone una metodología completa para la identificación y caracterización de EMDs utilizando imágenes TCO. Para ello, el sistema desarrollado combina y explota diferentes conocimientos clínicos con estrategias de procesamiento de imágenes y aprendizaje automático. Este sistema automático es capaz de identificar y caracterizar las principales estructuras retinianas y diferentes afecciones patológicas asociadas con el EMD, siguiendo la clasificación clínica de referencia en el campo oftalmológico. A pesar de la complejidad de esta relevante patología ocular, el sistema propuesto logró resultados satisfactorios, demostrando ser lo sufi cientemente robusto como para ser usado en la práctica clínica diaria, ayudando a los médicos a producir diagnósticos más precisos y tratamientos más adecuados.[Resumo] O Edema Macular Diabético ( EMD) é unha das complicacións máis importantes da diabetes e unha das principais causas de cegueira prevenible nos países desenvoltos. Entre as diferentes modalidades de imaxe, a Tomografía de Coherencia Óptica ( TCO) é unha técnica de imaxe non invasiva, transversal e de alta resolución que se usa comunmente para a análise e interpretación de múltiples estruturas retinianas e trastornos oculares. Desta maneira, o desenvolvemento dos sistemas de Diagnóstico Asistido por Computador ( DAO) volveuse relevante nos últimos anos, facilitando e simplificando o traballo dos especialistas clínicos en moitos procesos diagnósticos relevantes, substituíndo procedementos manuais que son tediosos e requiren moito tempo. Esta tese propón unha metodoloxía completa para a identificación e caracterización de EMDs utilizando imaxes TCO. Para iso, o sistema desenvolto combina e explota diferentes coñecementos clínicos con estratexias de procesamento de imaxes e aprendizaxe automático. Este sistema automático é capaz de identificar e caracterizar as principais estruturas retinianas e diferentes afeccións patolóxicas asociadas co EMD, seguindo a clasificación clínica de referencia no campo oftalmolóxico. A pesar da complexidade desta relevante patoloxía ocular, o sistema proposto logrou resultados satisfactorios, demostrando ser o sufi cientemente robusto como para ser usado na práctica clínica diaria, axudando aos médicos para producir diagnósticos máis precisos e tratamentos máis adecuados

Mobile Ad-Hoc Networks

Being infrastructure-less and without central administration control, wireless ad-hoc networking is playing a more and more important role in extending the coverage of traditional wireless infrastructure (cellular networks, wireless LAN, etc). This book includes state-of the-art techniques and solutions for wireless ad-hoc networks. It focuses on the following topics in ad-hoc networks: vehicular ad-hoc networks, security and caching, TCP in ad-hoc networks and emerging applications. It is targeted to provide network engineers and researchers with design guidelines for large scale wireless ad hoc networks