2,967 research outputs found

    A volumetric display for visual, tactile and audio presentation using acoustic trapping

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    Science-fiction movies such as Star Wars portray volumetric systems that not only provide visual but also tactile and audible 3D content. Displays, based on swept volume surfaces, holography, optophoretics, plasmonics, or lenticular lenslets, can create 3D visual content without the need for glasses or additional instrumentation. However, they are slow, have limited persistence of vision (POV) capabilities, and, most critically, rely on operating principles that cannot also produce tactile and auditive content. Here, we present for the first time a Multimodal Acoustic Trap Display (MATD): a mid-air volumetric display that can simultaneously deliver visual, auditory, and tactile content, using acoustophoresis as the single operating principle. Our system acoustically traps a particle and illuminates it with red, green, and blue light to control its colour as it quickly scans through our display volume. Using time multiplexing with a secondary trap, amplitude modulation and phase minimization, the MATD delivers simultaneous auditive and tactile content. The system demonstrates particle speeds of up to 8.75m/s and 3.75m/s in the vertical and horizontal directions respectively, offering particle manipulation capabilities superior to other optical or acoustic approaches demonstrated to date. Beyond enabling simultaneous visual, tactile and auditive content, our approach and techniques offer opportunities for non-contact, high-speed manipulation of matter, with applications in computational fabrication and biomedicine

    Unobtrusive and pervasive video-based eye-gaze tracking

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    Eye-gaze tracking has long been considered a desktop technology that finds its use inside the traditional office setting, where the operating conditions may be controlled. Nonetheless, recent advancements in mobile technology and a growing interest in capturing natural human behaviour have motivated an emerging interest in tracking eye movements within unconstrained real-life conditions, referred to as pervasive eye-gaze tracking. This critical review focuses on emerging passive and unobtrusive video-based eye-gaze tracking methods in recent literature, with the aim to identify different research avenues that are being followed in response to the challenges of pervasive eye-gaze tracking. Different eye-gaze tracking approaches are discussed in order to bring out their strengths and weaknesses, and to identify any limitations, within the context of pervasive eye-gaze tracking, that have yet to be considered by the computer vision community.peer-reviewe

    A Review and Analysis of Eye-Gaze Estimation Systems, Algorithms and Performance Evaluation Methods in Consumer Platforms

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    In this paper a review is presented of the research on eye gaze estimation techniques and applications, that has progressed in diverse ways over the past two decades. Several generic eye gaze use-cases are identified: desktop, TV, head-mounted, automotive and handheld devices. Analysis of the literature leads to the identification of several platform specific factors that influence gaze tracking accuracy. A key outcome from this review is the realization of a need to develop standardized methodologies for performance evaluation of gaze tracking systems and achieve consistency in their specification and comparative evaluation. To address this need, the concept of a methodological framework for practical evaluation of different gaze tracking systems is proposed.Comment: 25 pages, 13 figures, Accepted for publication in IEEE Access in July 201

    Practical aspects of physical and MAC layer security in visible light communication systems

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    Abstract— Visible light communication (VLC) has been recently proposed as an alternative standard to radio-based wireless networks. Originally developed as a physical media for PANs (Personal area Networks) it evolved into universal WLAN technology with a capability to transport internet suite of network and application level protocols. Because of its physical characteristics, and in line with the slogan "what you see is what you send", VLC is considered a secure communication method. In this work we focus on security aspects of VLC communication, starting from basic physical characteristics of the communication channel. We analyze the risks of signal jamming, data snooping and data modification. We also discuss MAC-level security mechanisms as defined in the IEEE 802.15.7 standard. This paper is an extension of work originally reported in Proceedings of the 13th IFAC and IEEE Conference on Programmable Devices and Embedded Systems — PDES 2015

    Discovering user mobility and activity in smart lighting environments

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    "Smart lighting" environments seek to improve energy efficiency, human productivity and health by combining sensors, controls, and Internet-enabled lights with emerging “Internet-of-Things” technology. Interesting and potentially impactful applications involve adaptive lighting that responds to individual occupants' location, mobility and activity. In this dissertation, we focus on the recognition of user mobility and activity using sensing modalities and analytical techniques. This dissertation encompasses prior work using body-worn inertial sensors in one study, followed by smart-lighting inspired infrastructure sensors deployed with lights. The first approach employs wearable inertial sensors and body area networks that monitor human activities with a user's smart devices. Real-time algorithms are developed to (1) estimate angles of excess forward lean to prevent risk of falls, (2) identify functional activities, including postures, locomotion, and transitions, and (3) capture gait parameters. Two human activity datasets are collected from 10 healthy young adults and 297 elder subjects, respectively, for laboratory validation and real-world evaluation. Results show that these algorithms can identify all functional activities accurately with a sensitivity of 98.96% on the 10-subject dataset, and can detect walking activities and gait parameters consistently with high test-retest reliability (p-value < 0.001) on the 297-subject dataset. The second approach leverages pervasive "smart lighting" infrastructure to track human location and predict activities. A use case oriented design methodology is considered to guide the design of sensor operation parameters for localization performance metrics from a system perspective. Integrating a network of low-resolution time-of-flight sensors in ceiling fixtures, a recursive 3D location estimation formulation is established that links a physical indoor space to an analytical simulation framework. Based on indoor location information, a label-free clustering-based method is developed to learn user behaviors and activity patterns. Location datasets are collected when users are performing unconstrained and uninstructed activities in the smart lighting testbed under different layout configurations. Results show that the activity recognition performance measured in terms of CCR ranges from approximately 90% to 100% throughout a wide range of spatio-temporal resolutions on these location datasets, insensitive to the reconfiguration of environment layout and the presence of multiple users.2017-02-17T00:00:00

    Long Range Automated Persistent Surveillance

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    This dissertation addresses long range automated persistent surveillance with focus on three topics: sensor planning, size preserving tracking, and high magnification imaging. field of view should be reserved so that camera handoff can be executed successfully before the object of interest becomes unidentifiable or untraceable. We design a sensor planning algorithm that not only maximizes coverage but also ensures uniform and sufficient overlapped camera’s field of view for an optimal handoff success rate. This algorithm works for environments with multiple dynamic targets using different types of cameras. Significantly improved handoff success rates are illustrated via experiments using floor plans of various scales. Size preserving tracking automatically adjusts the camera’s zoom for a consistent view of the object of interest. Target scale estimation is carried out based on the paraperspective projection model which compensates for the center offset and considers system latency and tracking errors. A computationally efficient foreground segmentation strategy, 3D affine shapes, is proposed. The 3D affine shapes feature direct and real-time implementation and improved flexibility in accommodating the target’s 3D motion, including off-plane rotations. The effectiveness of the scale estimation and foreground segmentation algorithms is validated via both offline and real-time tracking of pedestrians at various resolution levels. Face image quality assessment and enhancement compensate for the performance degradations in face recognition rates caused by high system magnifications and long observation distances. A class of adaptive sharpness measures is proposed to evaluate and predict this degradation. A wavelet based enhancement algorithm with automated frame selection is developed and proves efficient by a considerably elevated face recognition rate for severely blurred long range face images

    A Morphable Face Albedo Model

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    In this paper, we bring together two divergent strands of research: photometric face capture and statistical 3D face appearance modelling. We propose a novel lightstage capture and processing pipeline for acquiring ear-to-ear, truly intrinsic diffuse and specular albedo maps that fully factor out the effects of illumination, camera and geometry. Using this pipeline, we capture a dataset of 50 scans and combine them with the only existing publicly available albedo dataset (3DRFE) of 23 scans. This allows us to build the first morphable face albedo model. We believe this is the first statistical analysis of the variability of facial specular albedo maps. This model can be used as a plug in replacement for the texture model of the Basel Face Model (BFM) or FLAME and we make the model publicly available. We ensure careful spectral calibration such that our model is built in a linear sRGB space, suitable for inverse rendering of images taken by typical cameras. We demonstrate our model in a state of the art analysis-by-synthesis 3DMM fitting pipeline, are the first to integrate specular map estimation and outperform the BFM in albedo reconstruction.Comment: CVPR 202

    Robust iris recognition under unconstrained settings

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    Tese de mestrado integrado. Bioengenharia. Faculdade de Engenharia. Universidade do Porto. 201

    Modular multimodal platform for classical and high throughput light sheet microscopy

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    Light-sheet fluorescence microscopy (LSFM) has become an important tool for biological and biomedical research. Although several illumination and detection strategies have been developed, the sample mounting still represents a cumbersome procedure as this is highly dependent on the type of sample and often this might be time consuming. This prevents the use of LSFM in other promising applications in which a fast and straightforward sample-mounting procedure and imaging are essential. These include the high-throughput research fields, e.g. in drug screenings and toxicology studies. Here we present a new imaging paradigm for LSFM, which exploits modularity to offer multimodal imaging and straightforward sample mounting strategy, enhancing the flexibility and throughput of the system. We describe its implementation in which the sample can be imaged either as in any classical configuration, as it flows through the light-sheet using a fluidic approach, or a combination of both. We also evaluate its ability to image a variety of samples, from zebrafish embryos and larvae to 3D complex cell cultures.The authors acknowledge financial support from the Spanish Ministerio de Economía y Competitividad (MINECO) through the “Severo Ochoa” program for Centres of Excellence in R&D (CEX2019-000910-S [MCIN/ AEI/10.13039/501100011033]), Fundació Privada Cellex, Fundació Mir-Puig, and Generalitat de Catalunya through CERCA program; MINECO/FEDER Ramón y Cajal program (RYC-2015-17935); Laserlab- Europe EU-H2020 GA no. 871124; European Union’s Horizon 2020 Framework Programme (H2020 Marie Skłodowska-Curie Innovative Training Networks ImageInLife N. 721537). We thank Verena Ruprecht (CRG- Center of Genomic Regulation, Barcelona), Paz Herráez (Universidad de León), Ester Antón-Galindo and Noelia Fernández-Castillo (Universitat de Barcelona), Marymar Becerra (Universidad Nacional Autónoma de México), Georges Lutfalla, Mai Nguyen Chi and Tamara Sipka (Université de Montpellier), Catarina Brito (ITQB/IBEQ, Lisbon), Antonia Weberling and Magdalena Zernicka-Goetz (University of Cambridge), and Corinne Lorenzo (ITAV – CNRS, Toulouse) for the samples provided. We also thank Maria Marsal and Jordi Andilla for many fruitful discussions.Postprint (published version

    Robust and real-time hand detection and tracking in monocular video

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    In recent years, personal computing devices such as laptops, tablets and smartphones have become ubiquitous. Moreover, intelligent sensors are being integrated into many consumer devices such as eyeglasses, wristwatches and smart televisions. With the advent of touchscreen technology, a new human-computer interaction (HCI) paradigm arose that allows users to interface with their device in an intuitive manner. Using simple gestures, such as swipe or pinch movements, a touchscreen can be used to directly interact with a virtual environment. Nevertheless, touchscreens still form a physical barrier between the virtual interface and the real world. An increasingly popular field of research that tries to overcome this limitation, is video based gesture recognition, hand detection and hand tracking. Gesture based interaction allows the user to directly interact with the computer in a natural manner by exploring a virtual reality using nothing but his own body language. In this dissertation, we investigate how robust hand detection and tracking can be accomplished under real-time constraints. In the context of human-computer interaction, real-time is defined as both low latency and low complexity, such that a complete video frame can be processed before the next one becomes available. Furthermore, for practical applications, the algorithms should be robust to illumination changes, camera motion, and cluttered backgrounds in the scene. Finally, the system should be able to initialize automatically, and to detect and recover from tracking failure. We study a wide variety of existing algorithms, and propose significant improvements and novel methods to build a complete detection and tracking system that meets these requirements. Hand detection, hand tracking and hand segmentation are related yet technically different challenges. Whereas detection deals with finding an object in a static image, tracking considers temporal information and is used to track the position of an object over time, throughout a video sequence. Hand segmentation is the task of estimating the hand contour, thereby separating the object from its background. Detection of hands in individual video frames allows us to automatically initialize our tracking algorithm, and to detect and recover from tracking failure. Human hands are highly articulated objects, consisting of finger parts that are connected with joints. As a result, the appearance of a hand can vary greatly, depending on the assumed hand pose. Traditional detection algorithms often assume that the appearance of the object of interest can be described using a rigid model and therefore can not be used to robustly detect human hands. Therefore, we developed an algorithm that detects hands by exploiting their articulated nature. Instead of resorting to a template based approach, we probabilistically model the spatial relations between different hand parts, and the centroid of the hand. Detecting hand parts, such as fingertips, is much easier than detecting a complete hand. Based on our model of the spatial configuration of hand parts, the detected parts can be used to obtain an estimate of the complete hand's position. To comply with the real-time constraints, we developed techniques to speed-up the process by efficiently discarding unimportant information in the image. Experimental results show that our method is competitive with the state-of-the-art in object detection while providing a reduction in computational complexity with a factor 1 000. Furthermore, we showed that our algorithm can also be used to detect other articulated objects such as persons or animals and is therefore not restricted to the task of hand detection. Once a hand has been detected, a tracking algorithm can be used to continuously track its position in time. We developed a probabilistic tracking method that can cope with uncertainty caused by image noise, incorrect detections, changing illumination, and camera motion. Furthermore, our tracking system automatically determines the number of hands in the scene, and can cope with hands entering or leaving the video canvas. We introduced several novel techniques that greatly increase tracking robustness, and that can also be applied in other domains than hand tracking. To achieve real-time processing, we investigated several techniques to reduce the search space of the problem, and deliberately employ methods that are easily parallelized on modern hardware. Experimental results indicate that our methods outperform the state-of-the-art in hand tracking, while providing a much lower computational complexity. One of the methods used by our probabilistic tracking algorithm, is optical flow estimation. Optical flow is defined as a 2D vector field describing the apparent velocities of objects in a 3D scene, projected onto the image plane. Optical flow is known to be used by many insects and birds to visually track objects and to estimate their ego-motion. However, most optical flow estimation methods described in literature are either too slow to be used in real-time applications, or are not robust to illumination changes and fast motion. We therefore developed an optical flow algorithm that can cope with large displacements, and that is illumination independent. Furthermore, we introduce a regularization technique that ensures a smooth flow-field. This regularization scheme effectively reduces the number of noisy and incorrect flow-vector estimates, while maintaining the ability to handle motion discontinuities caused by object boundaries in the scene. The above methods are combined into a hand tracking framework which can be used for interactive applications in unconstrained environments. To demonstrate the possibilities of gesture based human-computer interaction, we developed a new type of computer display. This display is completely transparent, allowing multiple users to perform collaborative tasks while maintaining eye contact. Furthermore, our display produces an image that seems to float in thin air, such that users can touch the virtual image with their hands. This floating imaging display has been showcased on several national and international events and tradeshows. The research that is described in this dissertation has been evaluated thoroughly by comparing detection and tracking results with those obtained by state-of-the-art algorithms. These comparisons show that the proposed methods outperform most algorithms in terms of accuracy, while achieving a much lower computational complexity, resulting in a real-time implementation. Results are discussed in depth at the end of each chapter. This research further resulted in an international journal publication; a second journal paper that has been submitted and is under review at the time of writing this dissertation; nine international conference publications; a national conference publication; a commercial license agreement concerning the research results; two hardware prototypes of a new type of computer display; and a software demonstrator
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