2,146 research outputs found

    Impact of Imaging and Distance Perception in VR Immersive Visual Experience

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    Virtual reality (VR) headsets have evolved to include unprecedented viewing quality. Meanwhile, they have become lightweight, wireless, and low-cost, which has opened to new applications and a much wider audience. VR headsets can now provide users with greater understanding of events and accuracy of observation, making decision-making faster and more effective. However, the spread of immersive technologies has shown a slow take-up, with the adoption of virtual reality limited to a few applications, typically related to entertainment. This reluctance appears to be due to the often-necessary change of operating paradigm and some scepticism towards the "VR advantage". The need therefore arises to evaluate the contribution that a VR system can make to user performance, for example to monitoring and decision-making. This will help system designers understand when immersive technologies can be proposed to replace or complement standard display systems such as a desktop monitor. In parallel to the VR headsets evolution there has been that of 360 cameras, which are now capable to instantly acquire photographs and videos in stereoscopic 3D (S3D) modality, with very high resolutions. 360° images are innately suited to VR headsets, where the captured view can be observed and explored through the natural rotation of the head. Acquired views can even be experienced and navigated from the inside as they are captured. The combination of omnidirectional images and VR headsets has opened to a new way of creating immersive visual representations. We call it: photo-based VR. This represents a new methodology that combines traditional model-based rendering with high-quality omnidirectional texture-mapping. Photo-based VR is particularly suitable for applications related to remote visits and realistic scene reconstruction, useful for monitoring and surveillance systems, control panels and operator training. The presented PhD study investigates the potential of photo-based VR representations. It starts by evaluating the role of immersion and user’s performance in today's graphical visual experience, to then use it as a reference to develop and evaluate new photo-based VR solutions. With the current literature on photo-based VR experience and associated user performance being very limited, this study builds new knowledge from the proposed assessments. We conduct five user studies on a few representative applications examining how visual representations can be affected by system factors (camera and display related) and how it can influence human factors (such as realism, presence, and emotions). Particular attention is paid to realistic depth perception, to support which we develop target solutions for photo-based VR. They are intended to provide users with a correct perception of space dimension and objects size. We call it: true-dimensional visualization. The presented work contributes to unexplored fields including photo-based VR and true-dimensional visualization, offering immersive system designers a thorough comprehension of the benefits, potential, and type of applications in which these new methods can make the difference. This thesis manuscript and its findings have been partly presented in scientific publications. In particular, five conference papers on Springer and the IEEE symposia, [1], [2], [3], [4], [5], and one journal article in an IEEE periodical [6], have been published

    Current Challenges and Advances in Cataract Surgery

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    This reprint focuses on new trials related to cataract surgery, intraocular lens power calculations for cataracts after refractive surgery, problems related to high myopia, toric IOL power calculations, etc. Intraoperative use of the 3D Viewing System and OCT, studies on the spectacle dependence of EDOF, IOL fixation status and visual function, and dry eye after FLAC are also discussed. Proteomic analysis of aqueous humor proteins is also discussed

    Une méthode de mesure du mouvement humain pour la programmation par démonstration

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    Programming by demonstration (PbD) is an intuitive approach to impart a task to a robot from one or several demonstrations by the human teacher. The acquisition of the demonstrations involves the solution of the correspondence problem when the teacher and the learner differ in sensing and actuation. Kinesthetic guidance is widely used to perform demonstrations. With such a method, the robot is manipulated by the teacher and the demonstrations are recorded by the robot's encoders. In this way, the correspondence problem is trivial but the teacher dexterity is afflicted which may impact the PbD process. Methods that are more practical for the teacher usually require the identification of some mappings to solve the correspondence problem. The demonstration acquisition method is based on a compromise between the difficulty of identifying these mappings, the level of accuracy of the recorded elements and the user-friendliness and convenience for the teacher. This thesis proposes an inertial human motion tracking method based on inertial measurement units (IMUs) for PbD for pick-and-place tasks. Compared to kinesthetic guidance, IMUs are convenient and easy to use but can present a limited accuracy. Their potential for PbD applications is investigated. To estimate the trajectory of the teacher's hand, 3 IMUs are placed on her/his arm segments (arm, forearm and hand) to estimate their orientations. A specific method is proposed to partially compensate the well-known drift of the sensor orientation estimation around the gravity direction by exploiting the particular configuration of the demonstration. This method, called heading reset, is based on the assumption that the sensor passes through its original heading with stationary phases several times during the demonstration. The heading reset is implemented in an integration and vector observation algorithm. Several experiments illustrate the advantages of this heading reset. A comprehensive inertial human hand motion tracking (IHMT) method for PbD is then developed. It includes an initialization procedure to estimate the orientation of each sensor with respect to the human arm segment and the initial orientation of the sensor with respect to the teacher attached frame. The procedure involves a rotation and a static position of the extended arm. The measurement system is thus robust with respect to the positioning of the sensors on the segments. A procedure for estimating the position of the human teacher relative to the robot and a calibration procedure for the parameters of the method are also proposed. At the end, the error of the human hand trajectory is measured experimentally and is found in an interval between 28.528.5 mm and 61.861.8 mm. The mappings to solve the correspondence problem are identified. Unfortunately, the observed level of accuracy of this IHMT method is not sufficient for a PbD process. In order to reach the necessary level of accuracy, a method is proposed to correct the hand trajectory obtained by IHMT using vision data. A vision system presents a certain complementarity with inertial sensors. For the sake of simplicity and robustness, the vision system only tracks the objects but not the teacher. The correction is based on so-called Positions Of Interest (POIs) and involves 3 steps: the identification of the POIs in the inertial and vision data, the pairing of the hand POIs to objects POIs that correspond to the same action in the task, and finally, the correction of the hand trajectory based on the pairs of POIs. The complete method for demonstration acquisition is experimentally evaluated in a full PbD process. This experiment reveals the advantages of the proposed method over kinesthesy in the context of this work.La programmation par démonstration est une approche intuitive permettant de transmettre une tâche à un robot à partir d'une ou plusieurs démonstrations faites par un enseignant humain. L'acquisition des démonstrations nécessite cependant la résolution d'un problème de correspondance quand les systèmes sensitifs et moteurs de l'enseignant et de l'apprenant diffèrent. De nombreux travaux utilisent des démonstrations faites par kinesthésie, i.e., l'enseignant manipule directement le robot pour lui faire faire la tâche. Ce dernier enregistre ses mouvements grâce à ses propres encodeurs. De cette façon, le problème de correspondance est trivial. Lors de telles démonstrations, la dextérité de l'enseignant peut être altérée et impacter tout le processus de programmation par démonstration. Les méthodes d'acquisition de démonstration moins invalidantes pour l'enseignant nécessitent souvent des procédures spécifiques pour résoudre le problème de correspondance. Ainsi l'acquisition des démonstrations se base sur un compromis entre complexité de ces procédures, le niveau de précision des éléments enregistrés et la commodité pour l'enseignant. Cette thèse propose ainsi une méthode de mesure du mouvement humain par capteurs inertiels pour la programmation par démonstration de tâches de ``pick-and-place''. Les capteurs inertiels sont en effet pratiques et faciles à utiliser, mais sont d'une précision limitée. Nous étudions leur potentiel pour la programmation par démonstration. Pour estimer la trajectoire de la main de l'enseignant, des capteurs inertiels sont placés sur son bras, son avant-bras et sa main afin d'estimer leurs orientations. Une méthode est proposée afin de compenser partiellement la dérive de l'estimation de l'orientation des capteurs autour de la direction de la gravité. Cette méthode, appelée ``heading reset'', est basée sur l'hypothèse que le capteur passe plusieurs fois par son azimut initial avec des phases stationnaires lors d'une démonstration. Cette méthode est implémentée dans un algorithme d'intégration et d'observation de vecteur. Des expériences illustrent les avantages du ``heading reset''. Cette thèse développe ensuite une méthode complète de mesure des mouvements de la main humaine par capteurs inertiels (IHMT). Elle comprend une première procédure d'initialisation pour estimer l'orientation des capteurs par rapport aux segments du bras humain ainsi que l'orientation initiale des capteurs par rapport au repère de référence de l'humain. Cette procédure, consistant en une rotation et une position statique du bras tendu, est robuste au positionnement des capteurs. Une seconde procédure est proposée pour estimer la position de l'humain par rapport au robot et pour calibrer les paramètres de la méthode. Finalement, l'erreur moyenne sur la trajectoire de la main humaine est mesurée expérimentalement entre 28.5 mm et 61.8 mm, ce qui n'est cependant pas suffisant pour la programmation par démonstration. Afin d'atteindre le niveau de précision nécessaire, une nouvelle méthode est développée afin de corriger la trajectoire de la main par IHMT à partir de données issues d'un système de vision, complémentaire des capteurs inertiels. Pour maintenir une certaine simplicité et robustesse, le système de vision ne suit que les objets et pas l'enseignant. La méthode de correction, basée sur des ``Positions Of Interest (POIs)'', est constituée de 3 étapes: l'identification des POIs dans les données issues des capteurs inertiels et du système de vision, puis l'association de POIs liées à la main et de POIs liées aux objets correspondant à la même action, et enfin, la correction de la trajectoire de la main à partir des paires de POIs. Finalement, la méthode IHMT corrigée est expérimentalement évaluée dans un processus complet de programmation par démonstration. Cette expérience montre l'avantage de la méthode proposée sur la kinesthésie dans le contexte de ce travail

    PlayMaps - Representation of 3D cartography data in Augmented Reality (AR) games

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    A presente dissertação tem como tema a representação de dados cartográficos 3D em ambientes de Realidade Aumentada (RA) aplicados a tecnologias móveis. No turismo, a escolha de um lugar para visitar pode ser um processo árduo e exaustivo que na maioria das vezes traz contratempos imprevistos. Dada a abundância de informações sobre um local, ou mesmo a falta dela em muitas circunstâncias, é simples para um possível turista ficar desorientado e fazer o julgamento incorreto de não visitá-lo. Em muitos casos, as pessoas até desconhecem o potencial das regiões. Muitos representantes têm lutado para encontrar maneiras de atrair mais turistas para suas cidades, muitas vezes se perguntando: "Como posso promover minha cidade para o mundo de uma maneira mais eficaz?". Obviamente, essa divulgação precisa ser feita de forma segura e acessível. Assim, esta dissertação pretende criar um jogo para adolescentes, entre os 10 e os 15 anos, baseado em Realidade Aumentada (RA), que seja capaz de digitalizar um mapa em papel e permitir ao utilizador interagir com o mesmo. A app terá de proporcionar a componente educativa do território e também um nível adequado de interação com o utilizador. Este aplicativo será fácil de usar e compatível com qualquer sistema operacional móvel. Como tal, foi pensado para que qualquer pessoa com um smartphone possa utilizar esta tecnologia. Além disso, este aplicativo será flexível, adaptável e genérico. Cartografias diferentes exigirão modelos geográficos diferentes ou mesmo metodologias de jogo diferentes. Portanto, este aplicativo terá a capacidade de importar diferentes informações geográficas e criar um jogo específico para essa cartografia. Dois estudos anteriores foram realizados nesta área, nomeadamente para potenciar a experiência de visitas à Galeria da Biodiversidade e ao Jardim Botânico como forma de jogo. O primeiro estudo utiliza o método "case study", utilizando uma abordagem quantitativa e qualitativa para entender como a experiência lúdica proporcionada pela tecnologia de realidade aumentada pode contribuir para a entrega de informações que esses espaços pretendem transmitir aos visitantes {soares2021flo}. O segundo estudo procurou explorar formas de conectar ambientes internos e externos por meio de Realidade Aumentada (RA) {teixeira2021serious}. Esses tipos de abordagens de como disseminar informações são fundamentais para que o usuário tenha uma compreensão adequada do que a região tem a oferecer. Esses recursos serão aplicados ao mapeamento de territórios conhecidos a fim de investigar formas de promover o turismo melhorando e inovando a forma como as pessoas aprendem e exploram novas cidades e outros espaços. Embora esta dissertação seja apenas um caso de estudo, com esta implementação, a solução proposta pode funcionar como base para o desenvolvimento de uma ferramenta universal para qualquer instituição e/ou cidade que se queira promover.The present dissertation has, as its theme, the representation of 3D cartography data in Augmented Reality (AR) environments applied to mobile technologies. In tourism, choosing a place to visit can be a hard and exhaustive process which most times brings unforeseen setbacks. Given the abundance of information about a location, or even the lack thereof in many circumstances, it's simple for a possible tourist to become disoriented and make the incorrect judgement of not visiting it. In many cases, people even are unaware of the regions' potential. Many city officials and space marketers have struggled to find ways to attract more tourists to their cities, often asking themselves, "How can I promote my city to the world in a more effective way?". Obviously, this advertisement needs to be done in a safe and affordable way. Therefore, this dissertation aims to create a game for teens, between 10 and 15 years old, based on Augmented Reality (AR), which is capable to digitise a paper map and allowing the user to interact with it. The app will have to provide the educational component of the territory and also an appropriate level of interaction with the user. This app will be easy to use, user-friendly and compatible with any mobile operating system. As such, it is designed that anyone with a smart phone can use this technology. Furthermore, this app will be flexible, adaptable and generic. Different cartographies will require different geographical models or even different game methodologies. Therefore, this application will have the ability to import different geographic information, and create a specific game to that cartography. Two previous studies were carried out in this area, namely to enhance the experience of visits to Galeria da Biodiversidade and Jardim Botânico as a form of a game. The first study employs the case study method, using a quantitative and qualitative approach to comprehend how the entertaining experience given by augmented reality technology may contribute in the delivery of the information these spaces intend to transmit to visitors {soares2021flo}. The second study sought to explore ways to connect indoor and outdoor environments through Augmented Reality (AR) {teixeira2021serious}. These types of approaches on how to disseminate information are fundamental for the user to have an appropriate grasp of what the region has to offer. These functionalities will be applied to cartography of know territories in order to investigate ways to promote tourism by improving and innovating the way people learn and explore new cities and other spaces. Although this dissertation is only a case study, with this implementation, the proposed solution can become a stepping stone to the development of a universal tool for any institution and/or city that intends to promote itself

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

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    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden
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