Learning the surroundings: 3D scene understanding from omnidirectional images

Las redes neuronales se han extendido por todo el mundo, siendo utilizadas en una gran variedad de aplicaciones. Estos métodos son capaces de reconocer música y audio, generar textos completos a partir de ideas simples u obtener información detallada y relevante de imágenes y videos. Las posibilidades que ofrecen las redes neuronales y métodos de aprendizaje profundo son incontables, convirtiéndose en la principal herramienta de investigación y nuevas aplicaciones en nuestra vida diaria. Al mismo tiempo, las imágenes omnidireccionales se están extendiendo dentro de la industria y nuestra sociedad, causando que la visión omnidireccional gane atención. A partir de imágenes 360 capturamos toda la información que rodea a la cámara en una sola toma.La combinación del aprendizaje profundo y la visión omnidireccional ha atraído a muchos investigadores. A partir de una única imagen omnidireccional se obtiene suficiente información del entorno para que una red neuronal comprenda sus alrededores y pueda interactuar con el entorno. Para aplicaciones como navegación y conducción autónoma, el uso de cámaras omnidireccionales proporciona información en torno del robot, person o vehículo, mientras que las cámaras convencionales carecen de esta información contextual debido a su reducido campo de visión. Aunque algunas aplicaciones pueden incluir varias cámaras convencionales para aumentar el campo de visión del sistema, tareas en las que el peso es importante (P.ej. guiado de personas con discapacidad visual o navegación de drones autónomos), un número reducido de dispositivos es altamente deseable.En esta tesis nos centramos en el uso conjunto de cámaras omnidireccionales, aprendizaje profundo, geometría y fotometría. Evaluamos diferentes enfoques para tratar con imágenes omnidireccionales, adaptando métodos a los modelos de proyección omnidireccionales y proponiendo nuevas soluciones para afrontar los retos de este tipo de imágenes. Para la comprensión de entornos interiores, proponemos una nueva red neuronal que obtiene segmentación semántica y mapas de profundidad de forma conjunta a partir de un único panoramaequirectangular. Nuestra red logra, con un nuevo enfoque convolucional, aprovechar la información del entorno proporcionada por la imagen panorámica y explotar la información combinada de semántica y profundidad. En el mismo tema, combinamos aprendizaje profundo y soluciones geométricas para recuperar el diseño estructural, junto con su escala, de entornos de interior a partir de un único panorama no central. Esta combinación de métodos proporciona una implementación rápida, debido a la red neuronal, y resultados precisos, gracias a lassoluciones geométricas. Además, también proponemos varios enfoques para la adaptación de redes neuronales a la distorsión de modelos de proyección omnidireccionales para la navegación y la adaptación del dominio soluciones previas. En términos generales, esta tesis busca encontrar soluciones novedosas e innovadoras para aprovechar las ventajas de las cámaras omnidireccionales y superar los desafíos que plantean.Neural networks have become widespread all around the world and are used for many different applications. These new methods are able to recognize music and audio, generate full texts from simple ideas and obtain detailed and relevant information from images and videos. The possibilities of neural networks and deep learning methods are uncountable, becoming the main tool for research and new applications in our daily-life. At the same time, omnidirectional and 360 images are also becoming widespread in industry and in consumer society, causing omnidirectional computer vision to gain attention. From 360 images, we capture all the information surrounding the camera in a single shot. The combination of deep learning methods and omnidirectional computer vision have attracted many researchers to this new field. From a single omnidirectional image, we obtain enough information of the environment to make a neural network understand its surroundings and interact with the environment. For applications such as navigation and autonomous driving, the use of omnidirectional cameras provide information all around the robot, person or vehicle, while conventional perspective cameras lack this context information due to their narrow field of view. Even if some applications can include several conventional cameras to increase the system's field of view, tasks where weight is more important (i.e. guidance of visually impaired people or navigation of autonomous drones), the less cameras we need to include, the better. In this thesis, we focus in the joint use of omnidirectional cameras, deep learning, geometry and photometric methods. We evaluate different approaches to handle omnidirectional images, adapting previous methods to the distortion of omnidirectional projection models and also proposing new solutions to tackle the challenges of this kind of images. For indoor scene understanding, we propose a novel neural network that jointly obtains semantic segmentation and depth maps from single equirectangular panoramas. Our network manages, with a new convolutional approach, to leverage the context information provided by the panoramic image and exploit the combined information of semantics and depth. In the same topic, we combine deep learning and geometric solvers to recover the scaled structural layout of indoor environments from single non-central panoramas. This combination provides a fast implementation, thanks to the learning approach, and accurate result, due to the geometric solvers. Additionally, we also propose several approaches of network adaptation to the distortion of omnidirectional projection models for outdoor navigation and domain adaptation of previous solutions. All in all, this thesis looks for finding novel and innovative solutions to take advantage of omnidirectional cameras while overcoming the challenges they pose.<br /

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches

Modeling and Simulation in Engineering

This book provides an open platform to establish and share knowledge developed by scholars, scientists, and engineers from all over the world, about various applications of the modeling and simulation in the design process of products, in various engineering fields. The book consists of 12 chapters arranged in two sections (3D Modeling and Virtual Prototyping), reflecting the multidimensionality of applications related to modeling and simulation. Some of the most recent modeling and simulation techniques, as well as some of the most accurate and sophisticated software in treating complex systems, are applied. All the original contributions in this book are jointed by the basic principle of a successful modeling and simulation process: as complex as necessary, and as simple as possible. The idea is to manipulate the simplifying assumptions in a way that reduces the complexity of the model (in order to make a real-time simulation), but without altering the precision of the results

Camera positioning for 3D panoramic image rendering

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Virtual camera realisation and the proposition of trapezoidal camera architecture are the two broad contributions of this thesis. Firstly, multiple camera and their arrangement constitute a critical component which affect the integrity of visual content acquisition for multi-view video. Currently, linear, convergence, and divergence arrays are the prominent camera topologies adopted. However, the large number of cameras required and their synchronisation are two of prominent challenges usually encountered. The use of virtual cameras can significantly reduce the number of physical cameras used with respect to any of the known camera structures, hence adequately reducing some of the other implementation issues. This thesis explores to use image-based rendering with and without geometry in the implementations leading to the realisation of virtual cameras. The virtual camera implementation was carried out from the perspective of depth map (geometry) and use of multiple image samples (no geometry). Prior to the virtual camera realisation, the generation of depth map was investigated using region match measures widely known for solving image point correspondence problem. The constructed depth maps have been compare with the ones generated using the dynamic programming approach. In both the geometry and no geometry approaches, the virtual cameras lead to the rendering of views from a textured depth map, construction of 3D panoramic image of a scene by stitching multiple image samples and performing superposition on them, and computation of virtual scene from a stereo pair of panoramic images. The quality of these rendered images were assessed through the use of either objective or subjective analysis in Imatest software. Further more, metric reconstruction of a scene was performed by re-projection of the pixel points from multiple image samples with a single centre of projection. This was done using sparse bundle adjustment algorithm. The statistical summary obtained after the application of this algorithm provides a gauge for the efficiency of the optimisation step. The optimised data was then visualised in Meshlab software environment, hence providing the reconstructed scene. Secondly, with any of the well-established camera arrangements, all cameras are usually constrained to the same horizontal plane. Therefore, occlusion becomes an extremely challenging problem, and a robust camera set-up is required in order to resolve strongly the hidden part of any scene objects. To adequately meet the visibility condition for scene objects and given that occlusion of the same scene objects can occur, a multi-plane camera structure is highly desirable. Therefore, this thesis also explore trapezoidal camera structure for image acquisition. The approach here is to assess the feasibility and potential of several physical cameras of the same model being sparsely arranged on the edge of an efficient trapezoid graph. This is implemented both Matlab and Maya. The quality of the depth maps rendered in Matlab are better in Quality

Internet of Underwater Things and Big Marine Data Analytics -- A Comprehensive Survey

The Internet of Underwater Things (IoUT) is an emerging communication ecosystem developed for connecting underwater objects in maritime and underwater environments. The IoUT technology is intricately linked with intelligent boats and ships, smart shores and oceans, automatic marine transportations, positioning and navigation, underwater exploration, disaster prediction and prevention, as well as with intelligent monitoring and security. The IoUT has an influence at various scales ranging from a small scientific observatory, to a midsized harbor, and to covering global oceanic trade. The network architecture of IoUT is intrinsically heterogeneous and should be sufficiently resilient to operate in harsh environments. This creates major challenges in terms of underwater communications, whilst relying on limited energy resources. Additionally, the volume, velocity, and variety of data produced by sensors, hydrophones, and cameras in IoUT is enormous, giving rise to the concept of Big Marine Data (BMD), which has its own processing challenges. Hence, conventional data processing techniques will falter, and bespoke Machine Learning (ML) solutions have to be employed for automatically learning the specific BMD behavior and features facilitating knowledge extraction and decision support. The motivation of this paper is to comprehensively survey the IoUT, BMD, and their synthesis. It also aims for exploring the nexus of BMD with ML. We set out from underwater data collection and then discuss the family of IoUT data communication techniques with an emphasis on the state-of-the-art research challenges. We then review the suite of ML solutions suitable for BMD handling and analytics. We treat the subject deductively from an educational perspective, critically appraising the material surveyed.Comment: 54 pages, 11 figures, 19 tables, IEEE Communications Surveys & Tutorials, peer-reviewed academic journa

The Future of Humanoid Robots

This book provides state of the art scientific and engineering research findings and developments in the field of humanoid robotics and its applications. It is expected that humanoids will change the way we interact with machines, and will have the ability to blend perfectly into an environment already designed for humans. The book contains chapters that aim to discover the future abilities of humanoid robots by presenting a variety of integrated research in various scientific and engineering fields, such as locomotion, perception, adaptive behavior, human-robot interaction, neuroscience and machine learning. The book is designed to be accessible and practical, with an emphasis on useful information to those working in the fields of robotics, cognitive science, artificial intelligence, computational methods and other fields of science directly or indirectly related to the development and usage of future humanoid robots. The editor of the book has extensive R&D experience, patents, and publications in the area of humanoid robotics, and his experience is reflected in editing the content of the book

Multi-task near-field perception for autonomous driving using surround-view fisheye cameras

Die Bildung der Augen führte zum Urknall der Evolution. Die Dynamik änderte sich von einem primitiven Organismus, der auf den Kontakt mit der Nahrung wartete, zu einem Organismus, der durch visuelle Sensoren gesucht wurde. Das menschliche Auge ist eine der raffiniertesten Entwicklungen der Evolution, aber es hat immer noch Mängel. Der Mensch hat über Millionen von Jahren einen biologischen Wahrnehmungsalgorithmus entwickelt, der in der Lage ist, Autos zu fahren, Maschinen zu bedienen, Flugzeuge zu steuern und Schiffe zu navigieren. Die Automatisierung dieser Fähigkeiten für Computer ist entscheidend für verschiedene Anwendungen, darunter selbstfahrende Autos, Augmented Realität und architektonische Vermessung. Die visuelle Nahfeldwahrnehmung im Kontext von selbstfahrenden Autos kann die Umgebung in einem Bereich von 0 - 10 Metern und 360° Abdeckung um das Fahrzeug herum wahrnehmen. Sie ist eine entscheidende Entscheidungskomponente bei der Entwicklung eines sichereren automatisierten Fahrens. Jüngste Fortschritte im Bereich Computer Vision und Deep Learning in Verbindung mit hochwertigen Sensoren wie Kameras und LiDARs haben ausgereifte Lösungen für die visuelle Wahrnehmung hervorgebracht. Bisher stand die Fernfeldwahrnehmung im Vordergrund. Ein weiteres wichtiges Problem ist die begrenzte Rechenleistung, die für die Entwicklung von Echtzeit-Anwendungen zur Verfügung steht. Aufgrund dieses Engpasses kommt es häufig zu einem Kompromiss zwischen Leistung und Laufzeiteffizienz. Wir konzentrieren uns auf die folgenden Themen, um diese anzugehen: 1) Entwicklung von Nahfeld-Wahrnehmungsalgorithmen mit hoher Leistung und geringer Rechenkomplexität für verschiedene visuelle Wahrnehmungsaufgaben wie geometrische und semantische Aufgaben unter Verwendung von faltbaren neuronalen Netzen. 2) Verwendung von Multi-Task-Learning zur Überwindung von Rechenengpässen durch die gemeinsame Nutzung von initialen Faltungsschichten zwischen den Aufgaben und die Entwicklung von Optimierungsstrategien, die die Aufgaben ausbalancieren.The formation of eyes led to the big bang of evolution. The dynamics changed from a primitive organism waiting for the food to come into contact for eating food being sought after by visual sensors. The human eye is one of the most sophisticated developments of evolution, but it still has defects. Humans have evolved a biological perception algorithm capable of driving cars, operating machinery, piloting aircraft, and navigating ships over millions of years. Automating these capabilities for computers is critical for various applications, including self-driving cars, augmented reality, and architectural surveying. Near-field visual perception in the context of self-driving cars can perceive the environment in a range of 0 - 10 meters and 360° coverage around the vehicle. It is a critical decision-making component in the development of safer automated driving. Recent advances in computer vision and deep learning, in conjunction with high-quality sensors such as cameras and LiDARs, have fueled mature visual perception solutions. Until now, far-field perception has been the primary focus. Another significant issue is the limited processing power available for developing real-time applications. Because of this bottleneck, there is frequently a trade-off between performance and run-time efficiency. We concentrate on the following issues in order to address them: 1) Developing near-field perception algorithms with high performance and low computational complexity for various visual perception tasks such as geometric and semantic tasks using convolutional neural networks. 2) Using Multi-Task Learning to overcome computational bottlenecks by sharing initial convolutional layers between tasks and developing optimization strategies that balance tasks