2,421 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

    The efficacy of virtual reality in professional soccer

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    Professional soccer clubs have taken an interest to virtual reality, however, only a paucity of evidence exists to support its use in the soccer training ground environment. Further, several soccer virtual reality companies have begun providing solutions to teams, claiming to test specific characteristics of players, yet supportive evidence for certain measurement properties remain absent from the literature. The aims of this thesis were to explore the efficacy of virtual reality being used in the professional football training ground environment. To do so, this thesis looked to explore the fundamental measurement properties of soccer specific virtual reality tests, along with the perceptions of professional coaches, backroom staff, and players that could use virtual reality. The first research study (Chapter 3) aimed to quantify the learning effect during familiarisation trials of a soccer-specific virtual reality task. Thirty-four professional soccer players age, stature, and body mass: mean (SD) 20 (3.4) years; 180 (7) cm; 79 (8) kg, participated in six trials of a virtual reality soccer passing task. The task required participants to receive and pass 30 virtual soccer balls into highlighted mini-goals that surrounded the participant. The number of successful passes were recorded in each trial. The one-sided Bayesian paired samples t-test indicated very strong evidence in favour of the alternative hypothesis (H1)(BF10 = 46.5, d = 0.56 [95% CI = 0.2 to 0.92]) for improvements in total goals scored between trial 1: 13.6 (3.3) and trial 2: 16 (3.3). Further, the Bayesian paired-samples equivalence t-tests indicated strong evidence in favour of H1 (BF10 = 10.2, d = 0.24 [95% CI = -0.09 to 0.57]) for equivalence between trial 4: 16.7 (3.7) and trial 5: 18.2 (4.7); extreme evidence in favour of H1 (BF10 = 132, d = -0.02 [95% CI = -0.34 to 0.30]) for equivalence between trials 5 and 6: 18.1 (3.5); and moderate evidence in favour of H1 (BF10 = 8.4, d = 0.26 [95% CI = -0.08 to 0.59]) for equivalence between trials 4 and 6. Sufficient evidence indicated that a learning effect took place between the first two trials, and that up to five trials might be necessary for performance to plateau in a specific virtual reality soccer passing task.The second research study (Chapter 4) aimed to assess the validity of a soccer passing task by comparing passing ability between virtual reality and real-world conditions. A previously validated soccer passing test was replicated into a virtual reality environment. Twenty-nine soccer players participated in the study which required them to complete as many passes as possible between two rebound boards within 45 s. Counterbalancing determined the condition order, and then for each condition, participants completed four familiarisation trials and two recorded trials, with the best score being used for analysis. Sense of presence and fidelity were also assessed via questionnaires to understand how representative the virtual environments were compared to the real-world. Results showed that between conditions a difference was observed (EMM = -3.9, 95% HDI = -5.1 to -2.7) with the number of passes being greater in the real-world (EMM = 19.7, 95% HDI = 18.6 to 20.7) than in virtual reality (EMM = 15.7, 95% HDI = 14.7 to 16.8). Further, several subjective differences for fidelity between the two conditions were reported, notably the ability to control the ball in virtual reality which was suggested to have been more difficult than in the real-world. The last research study (Chapter 5) aimed to compare and quantify the perceptions of virtual reality use in soccer, and to model behavioural intentions to use this technology. This study surveyed the perceptions of coaches, support staff, and players in relation to their knowledge, expectations, influences, and barriers of using virtual reality via an internet-based questionnaire. To model behavioural intention, modified questions and constructs from the Unified Theory of Acceptance and Use of Technology were used, and the model was analysed through partial least squares structural equation modelling. Respondents represented coaches and support staff (n = 134) and players (n = 64). All respondents generally agreed that virtual reality should be used to improve tactical awareness and cognition, with its use primarily in performance analysis and rehabilitation settings. Generally, coaches and support staff agreed that monetary cost, coach buy-in and limited evidence base were barriers towards its use. In a sub-sample of coaches and support staff without access to virtual reality (n = 123), performance expectancy was the strongest construct in explaining behavioural intention to use virtual reality, followed by facilitating conditions (i.e., barriers) construct which had a negative association with behavioural intention. This thesis aimed to explore the measurement properties of soccer specific virtual reality tests, and the perceptions of staff and players who might use the technology. The key findings from exploring the measurement properties were (1) evidence of a learning curve, suggesting the need for multiple familiarisation trials before collecting data, and (2) a lack of evidence to support the validity of a virtual reality soccer passing test as evident by a lack of agreement to a real-world equivalent. This finding raises questions on the suitability for virtual reality being used to measure passing skill related performance. The key findings from investigating the perceptions of users included, using the technology to improve cognition and tactical awareness, and using it in rehabilitation and performance analysis settings. Future intention to use was generally positive, and driven by performance related factors, yet several barriers exist that may prevent its widespread use. In Chapter 7 of the thesis, a reflective account is presented for the reader, detailing some of the interactions made with coaches, support staff and players in relation to the personal, moral, and ethical challenges faced as a practitioner-researcher, working and studying, respectively, in a professional soccer club

    Scalable Exploration of Complex Objects and Environments Beyond Plain Visual Replication​

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    Digital multimedia content and presentation means are rapidly increasing their sophistication and are now capable of describing detailed representations of the physical world. 3D exploration experiences allow people to appreciate, understand and interact with intrinsically virtual objects. Communicating information on objects requires the ability to explore them under different angles, as well as to mix highly photorealistic or illustrative presentations of the object themselves with additional data that provides additional insights on these objects, typically represented in the form of annotations. Effectively providing these capabilities requires the solution of important problems in visualization and user interaction. In this thesis, I studied these problems in the cultural heritage-computing-domain, focusing on the very common and important special case of mostly planar, but visually, geometrically, and semantically rich objects. These could be generally roughly flat objects with a standard frontal viewing direction (e.g., paintings, inscriptions, bas-reliefs), as well as visualizations of fully 3D objects from a particular point of views (e.g., canonical views of buildings or statues). Selecting a precise application domain and a specific presentation mode allowed me to concentrate on the well defined use-case of the exploration of annotated relightable stratigraphic models (in particular, for local and remote museum presentation). My main results and contributions to the state of the art have been a novel technique for interactively controlling visualization lenses while automatically maintaining good focus-and-context parameters, a novel approach for avoiding clutter in an annotated model and for guiding users towards interesting areas, and a method for structuring audio-visual object annotations into a graph and for using that graph to improve guidance and support storytelling and automated tours. We demonstrated the effectiveness and potential of our techniques by performing interactive exploration sessions on various screen sizes and types ranging from desktop devices to large-screen displays for a walk-up-and-use museum installation. KEYWORDS - Computer Graphics, Human-Computer Interaction, Interactive Lenses, Focus-and-Context, Annotated Models, Cultural Heritage Computing

    Immersive Search: Comparing Conventional and Spatially Arranged Search Engine Result Pages in Immersive Virtual Environments

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    Advances in immersive technologies (e.g., virtual reality head-mounted displays) have brought a new dimension into user interfaces to increasingly more people in the recent years. However, little prior work has explored how people could use the extra dimension afforded by VR HMDs to aid in the information retrieval process. My dissertation research investigated how different task types and layouts of search engine result pages (displays) in immersive virtual environments impact the information retrieval process. In this dissertation, I present results from a within-subjects user study to investigate users' search behaviors, system interactions, perceptions, and eye-tracking behaviors for four different spatial arrangements of search results (``list'' - a 2D list; ``curve3'' - a 3x3 grid; ``curve4'' - a 4x4 grid; and ``sphere'' - a 4x4 sphere) in a VR HMD across two different task types (Find All relevant, Pick 3 best). Thirty-two (32) participants completed 5 search trials in 8 experimental conditions (4 displays x 2 task types). Results show that: (1) participants were accepting of and performed well in the spatial displays (curve3, curve4, and sphere); (2) participants had a positional bias for the top or top left of SERPs; (3) the angle of search results and layouts influenced the navigation patterns used; (4) participants had a preference for physical navigation (e.g., head movement) over virtual navigation (e.g., scrolling) to view and compare search results, and (5) participants were less likely to perceive a rank order in the spatial displays where a clear scan path was not obvious to them.Doctor of Philosoph

    Artificial Intelligence: Development and Applications in Neurosurgery

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    The last decade has witnessed a significant increase in the relevance of artificial intelligence (AI) in neuroscience. Gaining notoriety from its potential to revolutionize medical decision making, data analytics, and clinical workflows, AI is poised to be increasingly implemented into neurosurgical practice. However, certain considerations pose significant challenges to its immediate and widespread implementation. Hence, this chapter will explore current developments in AI as it pertains to the field of clinical neuroscience, with a primary focus on neurosurgery. Additionally included is a brief discussion of important economic and ethical considerations related to the feasibility and implementation of AI-based technologies in neurosciences, including future horizons such as the operational integrations of human and non-human capabilities

    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

    Counter-terrorism in cyber–physical spaces:Best practices and technologies from the state of the art

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    Context: The demand for protection and security of physical spaces and urban areas increased with the escalation of terroristic attacks in recent years. We envision with the proposed cyber–physical systems and spaces, a city that would indeed become a smarter urbanistic object, proactively providing alerts and being protective against any threat. Objectives: This survey intend to provide a systematic multivocal literature survey comprised of an updated, comprehensive and timely overview of state of the art in counter-terrorism cyber–physical systems, hence aimed at the protection of cyber–physical spaces. Hence, provide guidelines to law enforcement agencies and practitioners providing a description of technologies and best practices for the protection of public spaces. Methods: We analyzed 112 papers collected from different online sources, both from the academic field and from websites and blogs ranging from 2004 till mid-2022. Results: (a) There is no one single bullet-proof solution available for the protection of public spaces. (b) From our analysis we found three major active fields for the protection of public spaces: Information Technologies, Architectural approaches, Organizational field. (c) While the academic suggest best practices and methodologies for the protection of urban areas, the market did not provide any type of implementation of such suggested approaches, which shows a lack of fertilization between academia and industry. Conclusion: The overall analysis has led us to state that there is no one single solution available, conversely, multiple methods and techniques can be put in place to guarantee safety and security in public spaces. The techniques range from architectural design to rethink the design of public spaces keeping security into account in continuity, to emerging technologies such as AI and predictive surveillance.</p

    Seamless Multimodal Biometrics for Continuous Personalised Wellbeing Monitoring

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    Artificially intelligent perception is increasingly present in the lives of every one of us. Vehicles are no exception, (...) In the near future, pattern recognition will have an even stronger role in vehicles, as self-driving cars will require automated ways to understand what is happening around (and within) them and act accordingly. (...) This doctoral work focused on advancing in-vehicle sensing through the research of novel computer vision and pattern recognition methodologies for both biometrics and wellbeing monitoring. The main focus has been on electrocardiogram (ECG) biometrics, a trait well-known for its potential for seamless driver monitoring. Major efforts were devoted to achieving improved performance in identification and identity verification in off-the-person scenarios, well-known for increased noise and variability. Here, end-to-end deep learning ECG biometric solutions were proposed and important topics were addressed such as cross-database and long-term performance, waveform relevance through explainability, and interlead conversion. Face biometrics, a natural complement to the ECG in seamless unconstrained scenarios, was also studied in this work. The open challenges of masked face recognition and interpretability in biometrics were tackled in an effort to evolve towards algorithms that are more transparent, trustworthy, and robust to significant occlusions. Within the topic of wellbeing monitoring, improved solutions to multimodal emotion recognition in groups of people and activity/violence recognition in in-vehicle scenarios were proposed. At last, we also proposed a novel way to learn template security within end-to-end models, dismissing additional separate encryption processes, and a self-supervised learning approach tailored to sequential data, in order to ensure data security and optimal performance. (...)Comment: Doctoral thesis presented and approved on the 21st of December 2022 to the University of Port

    Synthetic Aperture Radar (SAR) Meets Deep Learning

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    This reprint focuses on the application of the combination of synthetic aperture radars and depth learning technology. It aims to further promote the development of SAR image intelligent interpretation technology. A synthetic aperture radar (SAR) is an important active microwave imaging sensor, whose all-day and all-weather working capacity give it an important place in the remote sensing community. Since the United States launched the first SAR satellite, SAR has received much attention in the remote sensing community, e.g., in geological exploration, topographic mapping, disaster forecast, and traffic monitoring. It is valuable and meaningful, therefore, to study SAR-based remote sensing applications. In recent years, deep learning represented by convolution neural networks has promoted significant progress in the computer vision community, e.g., in face recognition, the driverless field and Internet of things (IoT). Deep learning can enable computational models with multiple processing layers to learn data representations with multiple-level abstractions. This can greatly improve the performance of various applications. This reprint provides a platform for researchers to handle the above significant challenges and present their innovative and cutting-edge research results when applying deep learning to SAR in various manuscript types, e.g., articles, letters, reviews and technical reports

    Human-Machine Collaboration in AI-Assisted Surgery: Balancing Autonomy and Expertise

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    Artificial Intelligence is already being actively utilized in some fields of medicine. Its entrance into the surgical realm is inevitable, sure to become an integral tool for surgeons in their operating rooms and in providing perioperative care. As the technology matures and AI-collaborative systems become more widely available to assist in surgery, the need to find a balance between machine autonomy and surgeon expertise will become clearer. This chapter reviews the factors that need to be held in consideration to find this equilibrium. It examines the question from the perspective of the surgeon and the machine individually, their current and future collaborations, as well as the obstacles that lie ahead
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