Videos in Context for Telecommunication and Spatial Browsing

The research presented in this thesis explores the use of videos embedded in panoramic imagery to transmit spatial and temporal information describing remote environments and their dynamics. Virtual environments (VEs) through which users can explore remote locations are rapidly emerging as a popular medium of presence and remote collaboration. However, capturing visual representation of locations to be used in VEs is usually a tedious process that requires either manual modelling of environments or the employment of specific hardware. Capturing environment dynamics is not straightforward either, and it is usually performed through specific tracking hardware. Similarly, browsing large unstructured video-collections with available tools is difficult, as the abundance of spatial and temporal information makes them hard to comprehend. At the same time, on a spectrum between 3D VEs and 2D images, panoramas lie in between, as they offer the same 2D images accessibility while preserving 3D virtual environments surrounding representation. For this reason, panoramas are an attractive basis for videoconferencing and browsing tools as they can relate several videos temporally and spatially. This research explores methods to acquire, fuse, render and stream data coming from heterogeneous cameras, with the help of panoramic imagery. Three distinct but interrelated questions are addressed. First, the thesis considers how spatially localised video can be used to increase the spatial information transmitted during video mediated communication, and if this improves quality of communication. Second, the research asks whether videos in panoramic context can be used to convey spatial and temporal information of a remote place and the dynamics within, and if this improves users' performance in tasks that require spatio-temporal thinking. Finally, the thesis considers whether there is an impact of display type on reasoning about events within videos in panoramic context. These research questions were investigated over three experiments, covering scenarios common to computer-supported cooperative work and video browsing. To support the investigation, two distinct video+context systems were developed. The first telecommunication experiment compared our videos in context interface with fully-panoramic video and conventional webcam video conferencing in an object placement scenario. The second experiment investigated the impact of videos in panoramic context on quality of spatio-temporal thinking during localization tasks. To support the experiment, a novel interface to video-collection in panoramic context was developed and compared with common video-browsing tools. The final experimental study investigated the impact of display type on reasoning about events. The study explored three adaptations of our video-collection interface to three display types. The overall conclusion is that videos in panoramic context offer a valid solution to spatio-temporal exploration of remote locations. Our approach presents a richer visual representation in terms of space and time than standard tools, showing that providing panoramic contexts to video collections makes spatio-temporal tasks easier. To this end, videos in context are suitable alternative to more difficult, and often expensive solutions. These findings are beneficial to many applications, including teleconferencing, virtual tourism and remote assistance

AFFECT-PRESERVING VISUAL PRIVACY PROTECTION

The prevalence of wireless networks and the convenience of mobile cameras enable many new video applications other than security and entertainment. From behavioral diagnosis to wellness monitoring, cameras are increasing used for observations in various educational and medical settings. Videos collected for such applications are considered protected health information under privacy laws in many countries. Visual privacy protection techniques, such as blurring or object removal, can be used to mitigate privacy concern, but they also obliterate important visual cues of affect and social behaviors that are crucial for the target applications. In this dissertation, we propose to balance the privacy protection and the utility of the data by preserving the privacy-insensitive information, such as pose and expression, which is useful in many applications involving visual understanding. The Intellectual Merits of the dissertation include a novel framework for visual privacy protection by manipulating facial image and body shape of individuals, which: (1) is able to conceal the identity of individuals; (2) provide a way to preserve the utility of the data, such as expression and pose information; (3) balance the utility of the data and capacity of the privacy protection. The Broader Impacts of the dissertation focus on the significance of privacy protection on visual data, and the inadequacy of current privacy enhancing technologies in preserving affect and behavioral attributes of the visual content, which are highly useful for behavior observation in educational and medical settings. This work in this dissertation represents one of the first attempts in achieving both goals simultaneously

MONOCULAR POSE ESTIMATION AND SHAPE RECONSTRUCTION OF QUASI-ARTICULATED OBJECTS WITH CONSUMER DEPTH CAMERA

Quasi-articulated objects, such as human beings, are among the most commonly seen objects in our daily lives. Extensive research have been dedicated to 3D shape reconstruction and motion analysis for this type of objects for decades. A major motivation is their wide applications, such as in entertainment, surveillance and health care. Most of existing studies relied on one or more regular video cameras. In recent years, commodity depth sensors have become more and more widely available. The geometric measurements delivered by the depth sensors provide significantly valuable information for these tasks. In this dissertation, we propose three algorithms for monocular pose estimation and shape reconstruction of quasi-articulated objects using a single commodity depth sensor. These three algorithms achieve shape reconstruction with increasing levels of granularity and personalization. We then further develop a method for highly detailed shape reconstruction based on our pose estimation techniques. Our first algorithm takes advantage of a motion database acquired with an active marker-based motion capture system. This method combines pose detection through nearest neighbor search with pose refinement via non-rigid point cloud registration. It is capable of accommodating different body sizes and achieves more than twice higher accuracy compared to a previous state of the art on a publicly available dataset. The above algorithm performs frame by frame estimation and therefore is less prone to tracking failure. Nonetheless, it does not guarantee temporal consistent of the both the skeletal structure and the shape and could be problematic for some applications. To address this problem, we develop a real-time model-based approach for quasi-articulated pose and 3D shape estimation based on Iterative Closest Point (ICP) principal with several novel constraints that are critical for monocular scenario. In this algorithm, we further propose a novel method for automatic body size estimation that enables its capability to accommodate different subjects. Due to the local search nature, the ICP-based method could be trapped to local minima in the case of some complex and fast motions. To address this issue, we explore the potential of using statistical model for soft point correspondences association. Towards this end, we propose a unified framework based on Gaussian Mixture Model for joint pose and shape estimation of quasi-articulated objects. This method achieves state-of-the-art performance on various publicly available datasets. Based on our pose estimation techniques, we then develop a novel framework that achieves highly detailed shape reconstruction by only requiring the user to move naturally in front of a single depth sensor. Our experiments demonstrate reconstructed shapes with rich geometric details for various subjects with different apparels. Last but not the least, we explore the applicability of our method on two real-world applications. First of all, we combine our ICP-base method with cloth simulation techniques for Virtual Try-on. Our system delivers the first promising 3D-based virtual clothing system. Secondly, we explore the possibility to extend our pose estimation algorithms to assist physical therapist to identify their patients’ movement dysfunctions that are related to injuries. Our preliminary experiments have demonstrated promising results by comparison with the gold standard active marker-based commercial system. Throughout the dissertation, we develop various state-of-the-art algorithms for pose estimation and shape reconstruction of quasi-articulated objects by leveraging the geometric information from depth sensors. We also demonstrate their great potentials for different real-world applications

Augmented Reality for Restoration/Reconstruction of Artefacts with Artistic or Historical Value

The artistic or historical value of a structure, such as a monument, a mosaic, a painting or, generally speaking, an artefact, arises from the novelty and the development it represents in a certain field and in a certain time of the human activity. The more faithfully the structure preserves its original status, the greater its artistic and historical value is. For this reason it is fundamental to preserve its original condition, maintaining it as genuine as possible over the time. Nevertheless the preservation of a structure cannot be always possible (for traumatic events as wars can occur), or has not always been realized, simply for negligence, incompetence, or even guilty unwillingness. So, unfortunately, nowadays the status of a not irrelevant number of such structures can range from bad to even catastrophic. In such a frame the current technology furnishes a fundamental help for reconstruction/restoration purposes, so to bring back a structure to its original historical value and condition. Among the modern facilities, new possibilities arise from the Augmented Reality (AR) tools, which combine the virtual reality (VR) settings with real physical materials and instruments. The idea is to realize a virtual reconstruction/restoration before materially acting on the structure itself. In this way main advantages are obtained among which: the manpower and machine power are utilized only in the last phase of the reconstruction; potential damages/abrasions of some parts of the structure are avoided during the cataloguing phase; it is possible to precisely define the forms and dimensions of the eventually missing pieces, etc. Actually the virtual reconstruction/restoration can be even improved taking advantages of the AR, which furnish lots of added informative parameters, which can be even fundamental under specific circumstances. So we want here detail the AR application to restore and reconstruct the structures with artistic and/or historical valu

Augmented Reality

Augmented Reality (AR) is a natural development from virtual reality (VR), which was developed several decades earlier. AR complements VR in many ways. Due to the advantages of the user being able to see both the real and virtual objects simultaneously, AR is far more intuitive, but it's not completely detached from human factors and other restrictions. AR doesn't consume as much time and effort in the applications because it's not required to construct the entire virtual scene and the environment. In this book, several new and emerging application areas of AR are presented and divided into three sections. The first section contains applications in outdoor and mobile AR, such as construction, restoration, security and surveillance. The second section deals with AR in medical, biological, and human bodies. The third and final section contains a number of new and useful applications in daily living and learning

Técnicas de coste reducido para el posicionamiento del paciente en radioterapia percutánea utilizando un sistema de imágenes ópticas

Patient positioning is an important part of radiation therapy which is one of the main solutions for the treatment of malignant tissue in the human body. Currently, the most common patient positioning methods expose healthy tissue of the patient's body to extra dangerous radiations. Other non-invasive positioning methods are either not very accurate or are very costly for an average hospital. In this thesis, we explore the possibility of developing a system comprised of affordable hardware and advanced computer vision algorithms that facilitates patient positioning. Our algorithms are based on the usage of affordable RGB-D sensors, image features, ArUco planar markers, and other geometry registration methods. Furthermore, we take advantage of consumer-level computing hardware to make our systems widely accessible. More specifically, we avoid the usage of approaches that need to take advantage of dedicated GPU hardware for general-purpose computing since they are more costly. In different publications, we explore the usage of the mentioned tools to increase the accuracy of reconstruction/localization of the patient in its pose. We also take into account the visualization of the patient's target position with respect to their current position in order to assist the person who performs patient positioning. Furthermore, we make usage of augmented reality in conjunction with a real-time 3D tracking algorithm for better interaction between the program and the operator. We also solve more fundamental problems about ArUco markers that could be used in the future to improve our systems. These include highquality multi-camera calibration and mapping using ArUco markers plus detection of these markers in event cameras which are very useful in the presence of fast camera movement. In the end, we conclude that it is possible to increase the accuracy of 3D reconstruction and localization by combining current computer vision algorithms with fiducial planar markers with RGB-D sensors. This is reflected in the low amount of error we have achieved in our experiments for patient positioning, pushing forward the state of the art for this application.En el tratamiento de tumores malignos en el cuerpo, el posicionamiento del paciente en las sesiones de radioterapia es una cuestión crucial. Actualmente, los métodos más comunes de posicionamiento del paciente exponen tejido sano del mismo a radiaciones peligrosas debido a que no es posible asegurar que la posición del paciente siempre sea la misma que la que tuvo cuando se planificó la zona a radiar. Los métodos que se usan actualmente, o no son precisos o tienen costes que los hacen inasequibles para ser usados en hospitales con financiación limitada. En esta Tesis hemos analizado la posibilidad de desarrollar un sistema compuesto por hardware de bajo coste y métodos avanzados de visión por ordenador que ayuden a que el posicionamiento del paciente sea el mismo en las diferentes sesiones de radioterapia, con respecto a su pose cuando fue se planificó la zona a radiar. La solución propuesta como resultado de la Tesis se basa en el uso de sensores RGB-D, características extraídas de la imagen, marcadores cuadrados denominados ArUco y métodos de registro de la geometría en la imagen. Además, en la solución propuesta, se aprovecha la existencia de hardware convencional de bajo coste para hacer nuestro sistema ampliamente accesible. Más específicamente, evitamos el uso de enfoques que necesitan aprovechar GPU, de mayores costes, para computación de propósito general. Se han obtenido diferentes publicaciones para conseguir el objetivo final. Las mismas describen métodos para aumentar la precisión de la reconstrucción y la localización del paciente en su pose, teniendo en cuenta la visualización de la posición ideal del paciente con respecto a su posición actual, para ayudar al profesional que realiza la colocación del paciente. También se han propuesto métodos de realidad aumentada junto con algoritmos para seguimiento 3D en tiempo real para conseguir una mejor interacción entre el sistema ideado y el profesional que debe realizar esa labor. De forma añadida, también se han propuesto soluciones para problemas fundamentales relacionados con el uso de marcadores cuadrados que han sido utilizados para conseguir el objetivo de la Tesis. Las soluciones propuestas pueden ser empleadas en el futuro para mejorar otros sistemas. Los problemas citados incluyen la calibración y el mapeo multicámara de alta calidad utilizando los marcadores y la detección de estos marcadores en cámaras de eventos, que son muy útiles en presencia de movimientos rápidos de la cámara. Al final, concluimos que es posible aumentar la precisión de la reconstrucción y localización en 3D combinando los actuales algoritmos de visión por ordenador, que usan marcadores cuadrados de referencia, con sensores RGB-D. Los resultados obtenidos con respecto al error que el sistema obtiene al reproducir el posicionamiento del paciente suponen un importante avance en el estado del arte de este tópico

Peer Attention Modeling with Head Pose Trajectory Tracking Using Temporal Thermal Maps

Human head pose trajectories can represent a wealth of implicit information such as areas of attention, body language, potential future actions, and more. This signal is of high value for use in Human-Robot teams due to the implicit information encoded within it. Although team-based tasks require both explicit and implicit communication among peers, large team sizes, noisy environments, distance, and mission urgency can inhibit the frequency and quality of explicit communication. The goal for this thesis is to improve the capabilities of Human-Robot teams by making use of implicit communication. In support of this goal, the following hypotheses are investigated: ● Implicit information about a human subject’s attention can be reliably extracted with software by tracking the subject’s head pose trajectory, and ● Attention can be represented with a 3D temporal thermal map for implicitly determining a subject’s Objects Of Interest (OOIs). These hypotheses are investigated by experimentation with a new tool for peer attention modeling by Head Pose Trajectory Tracking using Temporal Thermal Maps (HPT4M). This system allows a robot Observing Agent (OA) to view a human teammate and temporally model their Regions Of Interest (ROIs) by generating a 3D thermal map based on the subject’s head pose trajectory. The findings in this work are that HPT4M can be used by an OA to contribute to a team search mission by implicitly discovering a human subject’s OOI type, mapping the item’s location within the searched space, and labeling the item’s discovery state. Furthermore, this work discusses some of the discovered limitations of this technology and hurdles that must be overcome before implementing HPT4M in a reliable real-world system. Finally, the techniques used in this work are provided as an open source Robot Operating System (ROS) node at github.com/HPT4M with the intent that it will aid other developers in the robotics community with improving Human-Robot teams. Furthermore, the proofs of principle and tools developed in this thesis are a foundational platform for deeper investigation in future research on improving Human-Robot teams via implicit communication techniques

High quality dynamic reflectance and surface reconstruction from video

The creation of high quality animations of real-world human actors has long been a challenging problem in computer graphics. It involves the modeling of the shape of the virtual actors, creating their motion, and the reproduction of very fine dynamic details. In order to render the actor under arbitrary lighting, it is required that reflectance properties are modeled for each point on the surface. These steps, that are usually performed manually by professional modelers, are time consuming and cumbersome. In this thesis, we show that algorithmic solutions for some of the problems that arise in the creation of high quality animation of real-world people are possible using multi-view video data. First, we present a novel spatio-temporal approach to create a personalized avatar from multi-view video data of a moving person. Thereafter, we propose two enhancements to a method that captures human shape, motion and reflectance properties of amoving human using eightmulti-view video streams. Afterwards we extend this work, and in order to add very fine dynamic details to the geometric models, such as wrinkles and folds in the clothing, we make use of the multi-view video recordings and present a statistical method that can passively capture the fine-grain details of time-varying scene geometry. Finally, in order to reconstruct structured shape and animation of the subject from video, we present a dense 3D correspondence finding method that enables spatiotemporally coherent reconstruction of surface animations directly frommulti-view video data. These algorithmic solutions can be combined to constitute a complete animation pipeline for acquisition, reconstruction and rendering of high quality virtual actors from multi-view video data. They can also be used individually in a system that require the solution of a specific algorithmic sub-problem. The results demonstrate that using multi-view video data it is possible to find the model description that enables realistic appearance of animated virtual actors under different lighting conditions and exhibits high quality dynamic details in the geometry.Die Entwicklung hochqualitativer Animationen von menschlichen Schauspielern ist seit langem ein schwieriges Problem in der Computergrafik. Es beinhaltet das Modellieren einer dreidimensionaler Abbildung des Akteurs, seiner Bewegung und die Wiedergabe sehr feiner dynamischer Details. Um den Schauspieler unter einer beliebigen Beleuchtung zu rendern, müssen auch die Reflektionseigenschaften jedes einzelnen Punktes modelliert werden. Diese Schritte, die gewöhnlich manuell von Berufsmodellierern durchgeführt werden, sind zeitaufwendig und beschwerlich. In dieser These schlagen wir algorithmische Lösungen für einige der Probleme vor, die in der Entwicklung solch hochqualitativen Animationen entstehen. Erstens präsentieren wir einen neuartigen, räumlich-zeitlichen Ansatz um einen Avatar von Mehransicht-Videodaten einer bewegenden Person zu schaffen. Danach beschreiben wir einen videobasierten Modelierungsansatz mit Hilfe einer animierten Schablone eines menschlichen Körpers. Unter Zuhilfenahme einer handvoll synchronisierter Videoaufnahmen berechnen wir die dreidimensionale Abbildung, seine Bewegung und Reflektionseigenschaften der Oberfläche. Um sehr feine dynamische Details, wie Runzeln und Falten in der Kleidung zu den geometrischen Modellen hinzuzufügen, zeigen wir eine statistische Methode, die feinen Details der zeitlich variierenden Szenegeometrie passiv erfassen kann. Und schließlich zeigen wir eine Methode, die dichte 3D Korrespondenzen findet, um die strukturierte Abbildung und die zugehörige Bewegung aus einem Video zu extrahieren. Dies ermöglicht eine räumlich-zeitlich zusammenhängende Rekonstruktion von Oberflächenanimationen direkt aus Mehransicht-Videodaten. Diese algorithmischen Lösungen können kombiniert eingesetzt werden, um eine Animationspipeline für die Erfassung, die Rekonstruktion und das Rendering von Animationen hoher Qualität aus Mehransicht-Videodaten zu ermöglichen. Sie können auch einzeln in einem System verwendet werden, das nach einer Lösung eines spezifischen algorithmischen Teilproblems verlangt. Das Ergebnis ist eine Modelbeschreibung, das realistisches Erscheinen von animierten virtuellen Schauspielern mit dynamischen Details von hoher Qualität unter verschiedenen Lichtverhältnissen ermöglicht

Towards Intelligent Telerobotics: Visualization and Control of Remote Robot

Human-machine cooperative or co-robotics has been recognized as the next generation of robotics. In contrast to current systems that use limited-reasoning strategies or address problems in narrow contexts, new co-robot systems will be characterized by their flexibility, resourcefulness, varied modeling or reasoning approaches, and use of real-world data in real time, demonstrating a level of intelligence and adaptability seen in humans and animals. The research I focused is in the two sub-field of co-robotics: teleoperation and telepresence. We firstly explore the ways of teleoperation using mixed reality techniques. I proposed a new type of display: hybrid-reality display (HRD) system, which utilizes commodity projection device to project captured video frame onto 3D replica of the actual target surface. It provides a direct alignment between the frame of reference for the human subject and that of the displayed image. The advantage of this approach lies in the fact that no wearing device needed for the users, providing minimal intrusiveness and accommodating users eyes during focusing. The field-of-view is also significantly increased. From a user-centered design standpoint, the HRD is motivated by teleoperation accidents, incidents, and user research in military reconnaissance etc. Teleoperation in these environments is compromised by the Keyhole Effect, which results from the limited field of view of reference. The technique contribution of the proposed HRD system is the multi-system calibration which mainly involves motion sensor, projector, cameras and robotic arm. Due to the purpose of the system, the accuracy of calibration should also be restricted within millimeter level. The followed up research of HRD is focused on high accuracy 3D reconstruction of the replica via commodity devices for better alignment of video frame. Conventional 3D scanner lacks either depth resolution or be very expensive. We proposed a structured light scanning based 3D sensing system with accuracy within 1 millimeter while robust to global illumination and surface reflection. Extensive user study prove the performance of our proposed algorithm. In order to compensate the unsynchronization between the local station and remote station due to latency introduced during data sensing and communication, 1-step-ahead predictive control algorithm is presented. The latency between human control and robot movement can be formulated as a linear equation group with a smooth coefficient ranging from 0 to 1. This predictive control algorithm can be further formulated by optimizing a cost function. We then explore the aspect of telepresence. Many hardware designs have been developed to allow a camera to be placed optically directly behind the screen. The purpose of such setups is to enable two-way video teleconferencing that maintains eye-contact. However, the image from the see-through camera usually exhibits a number of imaging artifacts such as low signal to noise ratio, incorrect color balance, and lost of details. Thus we develop a novel image enhancement framework that utilizes an auxiliary color+depth camera that is mounted on the side of the screen. By fusing the information from both cameras, we are able to significantly improve the quality of the see-through image. Experimental results have demonstrated that our fusion method compares favorably against traditional image enhancement/warping methods that uses only a single image