Virtual viewpoint three-dimensional panorama

Conventional panoramic images are known to provide for an enhanced field of view in which the scene always has a fixed appearance. The idea presented in this paper focuses on the use of the concept of virtual viewpoint creation to generate different panoramic images of the same scene with three-dimensional component. Three-dimensional effect in a resultant panorama is realized by superimposing a stereo-pair of panoramic images

Mutual Gaze Support in Videoconferencing Reviewed

Videoconferencing allows geographically dispersed parties to communicate by simultaneous audio and video transmissions. It is used in a variety of application scenarios with a wide range of coordination needs and efforts, such as private chat, discussion meetings, and negotiation tasks. In particular, in scenarios requiring certain levels of trust and judgement non-verbal communication, cues are highly important for effective communication. Mutual gaze support plays a central role in those high coordination need scenarios but generally lacks adequate technical support from videoconferencing systems. In this paper, we review technical concepts and implementations for mutual gaze support in videoconferencing, classify them, evaluate them according to a defined set of criteria, and give recommendations for future developments. Our review gives decision makers, researchers, and developers a tool to systematically apply and further develop videoconferencing systems in serious settings requiring mutual gaze. This should lead to well-informed decisions regarding the use and development of this technology and to a more widespread exploitation of the benefits of videoconferencing in general. For example, if videoconferencing systems supported high-quality mutual gaze in an easy-to-set-up and easy-to-use way, we could hold more effective and efficient recruitment interviews, court hearings, or contract negotiations

Face pose estimation with automatic 3D model creation for a driver inattention monitoring application

Texto en inglés y resumen en inglés y españolRecent studies have identified inattention (including distraction and drowsiness) as the main cause of accidents, being responsible of at least 25% of them. Driving distraction has been less studied, since it is more diverse and exhibits a higher risk factor than fatigue. In addition, it is present over half of the inattention involved crashes. The increased presence of In Vehicle Information Systems (IVIS) adds to the potential distraction risk and modifies driving behaviour, and thus research on this issue is of vital importance. Many researchers have been working on different approaches to deal with distraction during driving. Among them, Computer Vision is one of the most common, because it allows for a cost effective and non-invasive driver monitoring and sensing. Using Computer Vision techniques it is possible to evaluate some facial movements that characterise the state of attention of a driver. This thesis presents methods to estimate the face pose and gaze direction of a person in real-time, using a stereo camera as a basic for assessing driver distractions. The methods are completely automatic and user-independent. A set of features in the face are identified at initialisation, and used to create a sparse 3D model of the face. These features are tracked from frame to frame, and the model is augmented to cover parts of the face that may have been occluded before. The algorithm is designed to work in a naturalistic driving simulator, which presents challenging low light conditions. We evaluate several techniques to detect features on the face that can be matched between cameras and tracked with success. Well-known methods such as SURF do not return good results, due to the lack of salient points in the face, as well as the low illumination of the images. We introduce a novel multisize technique, based on Harris corner detector and patch correlation. This technique benefits from the better performance of small patches under rotations and illumination changes, and the more robust correlation of the bigger patches under motion blur. The head rotates in a range of ±90º in the yaw angle, and the appearance of the features change noticeably. To deal with these changes, we implement a new re-registering technique that captures new textures of the features as the face rotates. These new textures are incorporated to the model, which mixes the views of both cameras. The captures are taken at regular angle intervals for rotations in yaw, so that each texture is only used in a range of ±7.5º around the capture angle. Rotations in pitch and roll are handled using affine patch warping. The 3D model created at initialisation can only take features in the frontal part of the face, and some of these may occlude during rotations. The accuracy and robustness of the face tracking depends on the number of visible points, so new points are added to the 3D model when new parts of the face are visible from both cameras. Bundle adjustment is used to reduce the accumulated drift of the 3D reconstruction. We estimate the pose from the position of the features in the images and the 3D model using POSIT or Levenberg-Marquardt. A RANSAC process detects incorrectly tracked points, which are not considered for pose estimation. POSIT is faster, while LM obtains more accurate results. Using the model extension and the re-registering technique, we can accurately estimate the pose in the full head rotation range, with error levels that improve the state of the art. A coarse eye direction is composed with the face pose estimation to obtain the gaze and driver's fixation area, parameter which gives much information about the distraction pattern of the driver. The resulting gaze estimation algorithm proposed in this thesis has been tested on a set of driving experiments directed by a team of psychologists in a naturalistic driving simulator. This simulator mimics conditions present in real driving, including weather changes, manoeuvring and distractions due to IVIS. Professional drivers participated in the tests. The driver?s fixation statistics obtained with the proposed system show how the utilisation of IVIS influences the distraction pattern of the drivers, increasing reaction times and affecting the fixation of attention on the road and the surroundings

Face pose estimation with automatic 3D model creation for a driver inattention monitoring application

Texto en inglés y resumen en inglés y españolRecent studies have identified inattention (including distraction and drowsiness) as the main cause of accidents, being responsible of at least 25% of them. Driving distraction has been less studied, since it is more diverse and exhibits a higher risk factor than fatigue. In addition, it is present over half of the inattention involved crashes. The increased presence of In Vehicle Information Systems (IVIS) adds to the potential distraction risk and modifies driving behaviour, and thus research on this issue is of vital importance. Many researchers have been working on different approaches to deal with distraction during driving. Among them, Computer Vision is one of the most common, because it allows for a cost effective and non-invasive driver monitoring and sensing. Using Computer Vision techniques it is possible to evaluate some facial movements that characterise the state of attention of a driver. This thesis presents methods to estimate the face pose and gaze direction of a person in real-time, using a stereo camera as a basic for assessing driver distractions. The methods are completely automatic and user-independent. A set of features in the face are identified at initialisation, and used to create a sparse 3D model of the face. These features are tracked from frame to frame, and the model is augmented to cover parts of the face that may have been occluded before. The algorithm is designed to work in a naturalistic driving simulator, which presents challenging low light conditions. We evaluate several techniques to detect features on the face that can be matched between cameras and tracked with success. Well-known methods such as SURF do not return good results, due to the lack of salient points in the face, as well as the low illumination of the images. We introduce a novel multisize technique, based on Harris corner detector and patch correlation. This technique benefits from the better performance of small patches under rotations and illumination changes, and the more robust correlation of the bigger patches under motion blur. The head rotates in a range of ±90º in the yaw angle, and the appearance of the features change noticeably. To deal with these changes, we implement a new re-registering technique that captures new textures of the features as the face rotates. These new textures are incorporated to the model, which mixes the views of both cameras. The captures are taken at regular angle intervals for rotations in yaw, so that each texture is only used in a range of ±7.5º around the capture angle. Rotations in pitch and roll are handled using affine patch warping. The 3D model created at initialisation can only take features in the frontal part of the face, and some of these may occlude during rotations. The accuracy and robustness of the face tracking depends on the number of visible points, so new points are added to the 3D model when new parts of the face are visible from both cameras. Bundle adjustment is used to reduce the accumulated drift of the 3D reconstruction. We estimate the pose from the position of the features in the images and the 3D model using POSIT or Levenberg-Marquardt. A RANSAC process detects incorrectly tracked points, which are not considered for pose estimation. POSIT is faster, while LM obtains more accurate results. Using the model extension and the re-registering technique, we can accurately estimate the pose in the full head rotation range, with error levels that improve the state of the art. A coarse eye direction is composed with the face pose estimation to obtain the gaze and driver's fixation area, parameter which gives much information about the distraction pattern of the driver. The resulting gaze estimation algorithm proposed in this thesis has been tested on a set of driving experiments directed by a team of psychologists in a naturalistic driving simulator. This simulator mimics conditions present in real driving, including weather changes, manoeuvring and distractions due to IVIS. Professional drivers participated in the tests. The driver?s fixation statistics obtained with the proposed system show how the utilisation of IVIS influences the distraction pattern of the drivers, increasing reaction times and affecting the fixation of attention on the road and the surroundings

SELF-IMAGE MULTIMEDIA TECHNOLOGIES FOR FEEDFORWARD OBSERVATIONAL LEARNING

This dissertation investigates the development and use of self-images in augmented reality systems for learning and learning-based activities. This work focuses on self- modeling, a particular form of learning, actively employed in various settings for therapy or teaching. In particular, this work aims to develop novel multimedia systems to support the display and rendering of augmented self-images. It aims to use interactivity (via games) as a means of obtaining imagery for use in creating augmented self-images. Two multimedia systems are developed, discussed and analyzed. The proposed systems are validated in terms of their technical innovation and their clinical efficacy in delivering behavioral interventions for young children on the autism spectrum

Proceedings of the 2nd IUI Workshop on Interacting with Smart Objects

These are the Proceedings of the 2nd IUI Workshop on Interacting with Smart Objects. Objects that we use in our everyday life are expanding their restricted interaction capabilities and provide functionalities that go far beyond their original functionality. They feature computing capabilities and are thus able to capture information, process and store it and interact with their environments, turning them into smart objects

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

Natural image processing and synthesis using deep learning

Nous étudions dans cette thèse comment les réseaux de neurones profonds peuvent être utilisés dans différents domaines de la vision artificielle. La vision artificielle est un domaine interdisciplinaire qui traite de la compréhension d’images et de vidéos numériques. Les problèmes de ce domaine ont traditionnellement été adressés avec des méthodes ad-hoc nécessitant beaucoup de réglages manuels. En effet, ces systèmes de vision artificiels comprenaient jusqu’à récemment une série de modules optimisés indépendamment. Cette approche est très raisonnable dans la mesure où, avec peu de données, elle bénéficient autant que possible des connaissances du chercheur. Mais cette avantage peut se révéler être une limitation si certaines données d’entré n’ont pas été considérées dans la conception de l’algorithme. Avec des volumes et une diversité de données toujours plus grands, ainsi que des capacités de calcul plus rapides et économiques, les réseaux de neurones profonds optimisés d’un bout à l’autre sont devenus une alternative attrayante. Nous démontrons leur avantage avec une série d’articles de recherche, chacun d’entre eux trouvant une solution à base de réseaux de neurones profonds à un problème d’analyse ou de synthèse visuelle particulier. Dans le premier article, nous considérons un problème de vision classique: la détection de bords et de contours. Nous partons de l’approche classique et la rendons plus ‘neurale’ en combinant deux étapes, la détection et la description de motifs visuels, en un seul réseau convolutionnel. Cette méthode, qui peut ainsi s’adapter à de nouveaux ensembles de données, s’avère être au moins aussi précis que les méthodes conventionnelles quand il s’agit de domaines qui leur sont favorables, tout en étant beaucoup plus robuste dans des domaines plus générales. Dans le deuxième article, nous construisons une nouvelle architecture pour la manipulation d’images qui utilise l’idée que la majorité des pixels produits peuvent d’être copiés de l’image d’entrée. Cette technique bénéficie de plusieurs avantages majeurs par rapport à l’approche conventionnelle en apprentissage profond. En effet, elle conserve les détails de l’image d’origine, n’introduit pas d’aberrations grâce à la capacité limitée du réseau sous-jacent et simplifie l’apprentissage. Nous démontrons l’efficacité de cette architecture dans le cadre d’une tâche de correction du regard, où notre système produit d’excellents résultats. Dans le troisième article, nous nous éclipsons de la vision artificielle pour étudier le problème plus générale de l’adaptation à de nouveaux domaines. Nous développons un nouvel algorithme d’apprentissage, qui assure l’adaptation avec un objectif auxiliaire à la tâche principale. Nous cherchons ainsi à extraire des motifs qui permettent d’accomplir la tâche mais qui ne permettent pas à un réseau dédié de reconnaître le domaine. Ce réseau est optimisé de manière simultané avec les motifs en question, et a pour tâche de reconnaître le domaine de provenance des motifs. Cette technique est simple à implémenter, et conduit pourtant à l’état de l’art sur toutes les tâches de référence. Enfin, le quatrième article présente un nouveau type de modèle génératif d’images. À l’opposé des approches conventionnels à base de réseaux de neurones convolutionnels, notre système baptisé SPIRAL décrit les images en termes de programmes bas-niveau qui sont exécutés par un logiciel de graphisme ordinaire. Entre autres, ceci permet à l’algorithme de ne pas s’attarder sur les détails de l’image, et de se concentrer plutôt sur sa structure globale. L’espace latent de notre modèle est, par construction, interprétable et permet de manipuler des images de façon prévisible. Nous montrons la capacité et l’agilité de cette approche sur plusieurs bases de données de référence.In the present thesis, we study how deep neural networks can be applied to various tasks in computer vision. Computer vision is an interdisciplinary field that deals with understanding of digital images and video. Traditionally, the problems arising in this domain were tackled using heavily hand-engineered adhoc methods. A typical computer vision system up until recently consisted of a sequence of independent modules which barely talked to each other. Such an approach is quite reasonable in the case of limited data as it takes major advantage of the researcher's domain expertise. This strength turns into a weakness if some of the input scenarios are overlooked in the algorithm design process. With the rapidly increasing volumes and varieties of data and the advent of cheaper and faster computational resources end-to-end deep neural networks have become an appealing alternative to the traditional computer vision pipelines. We demonstrate this in a series of research articles, each of which considers a particular task of either image analysis or synthesis and presenting a solution based on a ``deep'' backbone. In the first article, we deal with a classic low-level vision problem of edge detection. Inspired by a top-performing non-neural approach, we take a step towards building an end-to-end system by combining feature extraction and description in a single convolutional network. The resulting fully data-driven method matches or surpasses the detection quality of the existing conventional approaches in the settings for which they were designed while being significantly more usable in the out-of-domain situations. In our second article, we introduce a custom architecture for image manipulation based on the idea that most of the pixels in the output image can be directly copied from the input. This technique bears several significant advantages over the naive black-box neural approach. It retains the level of detail of the original images, does not introduce artifacts due to insufficient capacity of the underlying neural network and simplifies training process, to name a few. We demonstrate the efficiency of the proposed architecture on the challenging gaze correction task where our system achieves excellent results. In the third article, we slightly diverge from pure computer vision and study a more general problem of domain adaption. There, we introduce a novel training-time algorithm (\ie, adaptation is attained by using an auxilliary objective in addition to the main one). We seek to extract features that maximally confuse a dedicated network called domain classifier while being useful for the task at hand. The domain classifier is learned simultaneosly with the features and attempts to tell whether those features are coming from the source or the target domain. The proposed technique is easy to implement, yet results in superior performance in all the standard benchmarks. Finally, the fourth article presents a new kind of generative model for image data. Unlike conventional neural network based approaches our system dubbed SPIRAL describes images in terms of concise low-level programs executed by off-the-shelf rendering software used by humans to create visual content. Among other things, this allows SPIRAL not to waste its capacity on minutae of datasets and focus more on the global structure. The latent space of our model is easily interpretable by design and provides means for predictable image manipulation. We test our approach on several popular datasets and demonstrate its power and flexibility

Activity profiling for minimally invasive surgery

Imperial Users onl