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  • 1
    Online Resource
    Online Resource
    Institute of Electrical and Electronics Engineers (IEEE) ; 2022
    In:  IEEE Transactions on Visualization and Computer Graphics Vol. 28, No. 11 ( 2022-11), p. 3854-3864
    In: IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers (IEEE), Vol. 28, No. 11 ( 2022-11), p. 3854-3864
    Type of Medium: Online Resource
    ISSN: 1077-2626 , 1941-0506 , 2160-9306
    RVK:
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2022
    detail.hit.zdb_id: 2027333-2
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  • 2
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2022
    In:  ACM Transactions on Graphics Vol. 41, No. 4 ( 2022-07), p. 1-15
    In: ACM Transactions on Graphics, Association for Computing Machinery (ACM), Vol. 41, No. 4 ( 2022-07), p. 1-15
    Abstract: We aim to ask and answer an essential question " how quickly do we react after observing a displayed visual target?" To this end, we present psychophysical studies that characterize the remarkable disconnect between human saccadic behaviors and spatial visual acuity. Building on the results of our studies, we develop a perceptual model to predict temporal gaze behavior, particularly saccadic latency, as a function of the statistics of a displayed image. Specifically, we implement a neurologically-inspired probabilistic model that mimics the accumulation of confidence that leads to a perceptual decision. We validate our model with a series of objective measurements and user studies using an eye-tracked VR display. The results demonstrate that our model prediction is in statistical alignment with real-world human behavior. Further, we establish that many sub-threshold image modifications commonly introduced in graphics pipelines may significantly alter human reaction timing, even if the differences are visually undetectable. Finally, we show that our model can serve as a metric to predict and alter reaction latency of users in interactive computer graphics applications, thus may improve gaze-contingent rendering, design of virtual experiences, and player performance in e-sports. We illustrate this with two examples: estimating competition fairness in a video game with two different team colors, and tuning display viewing distance to minimize player reaction time.
    Type of Medium: Online Resource
    ISSN: 0730-0301 , 1557-7368
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2022
    detail.hit.zdb_id: 2006336-2
    detail.hit.zdb_id: 625686-7
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  • 3
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2022
    In:  ACM Transactions on Graphics Vol. 41, No. 6 ( 2022-12), p. 1-15
    In: ACM Transactions on Graphics, Association for Computing Machinery (ACM), Vol. 41, No. 6 ( 2022-12), p. 1-15
    Abstract: Holographic displays promise to deliver unprecedented display capabilities in augmented reality applications, featuring a wide field of view, wide color gamut, spatial resolution, and depth cues all in a compact form factor. While emerging holographic display approaches have been successful in achieving large étendue and high image quality as seen by a camera, the large étendue also reveals a problem that makes existing displays impractical: the sampling of the holographic field by the eye pupil. Existing methods have not investigated this issue due to the lack of displays with large enough étendue, and, as such, they suffer from severe artifacts with varying eye pupil size and location. We show that the holographic field as sampled by the eye pupil is highly varying for existing display setups, and we propose pupil-aware holography that maximizes the perceptual image quality irrespective of the size, location, and orientation of the eye pupil in a near-eye holographic display. We validate the proposed approach both in simulations and on a prototype holographic display and show that our method eliminates severe artifacts and significantly outperforms existing approaches.
    Type of Medium: Online Resource
    ISSN: 0730-0301 , 1557-7368
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2022
    detail.hit.zdb_id: 2006336-2
    detail.hit.zdb_id: 625686-7
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  • 4
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2020
    In:  ACM Transactions on Graphics Vol. 39, No. 6 ( 2020-12-31), p. 1-18
    In: ACM Transactions on Graphics, Association for Computing Machinery (ACM), Vol. 39, No. 6 ( 2020-12-31), p. 1-18
    Abstract: Holography is arguably the most promising technology to provide wide field-of-view compact eyeglasses-style near-eye displays for augmented and virtual reality. However, the image quality of existing holographic displays is far from that of current generation conventional displays, effectively making today's holographic display systems impractical. This gap stems predominantly from the severe deviations in the idealized approximations of the "unknown" light transport model in a real holographic display, used for computing holograms. In this work, we depart from such approximate "ideal" coherent light transport models for computing holograms. Instead, we learn the deviations of the real display from the ideal light transport from the images measured using a display-camera hardware system. After this unknown light propagation is learned, we use it to compensate for severe aberrations in real holographic imagery. The proposed hardware-in-the-loop approach is robust to spatial, temporal and hardware deviations, and improves the image quality of existing methods qualitatively and quantitatively in SNR and perceptual quality. We validate our approach on a holographic display prototype and show that the method can fully compensate unknown aberrations and erroneous and non-linear SLM phase delays, without explicitly modeling them. As a result, the proposed method significantly outperforms existing state-of-the-art methods in simulation and experimentation - just by observing captured holographic images.
    Type of Medium: Online Resource
    ISSN: 0730-0301 , 1557-7368
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2020
    detail.hit.zdb_id: 2006336-2
    detail.hit.zdb_id: 625686-7
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  • 5
    Online Resource
    Online Resource
    Association for Computing Machinery (ACM) ; 2019
    In:  ACM Transactions on Graphics Vol. 38, No. 6 ( 2019-12-31), p. 1-13
    In: ACM Transactions on Graphics, Association for Computing Machinery (ACM), Vol. 38, No. 6 ( 2019-12-31), p. 1-13
    Abstract: Near-eye displays using holographic projection are emerging as an exciting display approach for virtual and augmented reality at high-resolution without complex optical setups --- shifting optical complexity to computation. While precise phase modulation hardware is becoming available, phase retrieval algorithms are still in their infancy, and holographic display approaches resort to heuristic encoding methods or iterative methods relying on various relaxations. In this work, we depart from such existing approximations and solve the phase retrieval problem for a hologram of a scene at a single depth at a given time by revisiting complex Wirtinger derivatives, also extending our framework to render 3D volumetric scenes. Using Wirtinger derivatives allows us to pose the phase retrieval problem as a quadratic problem which can be minimized with first-order optimization methods. The proposed Wirtinger Holography is flexible and facilitates the use of different loss functions, including learned perceptual losses parametrized by deep neural networks, as well as stochastic optimization methods. We validate this framework by demonstrating holographic reconstructions with an order of magnitude lower error, both in simulation and on an experimental hardware prototype.
    Type of Medium: Online Resource
    ISSN: 0730-0301 , 1557-7368
    Language: English
    Publisher: Association for Computing Machinery (ACM)
    Publication Date: 2019
    detail.hit.zdb_id: 2006336-2
    detail.hit.zdb_id: 625686-7
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  • 6
    Online Resource
    Online Resource
    Institute of Electrical and Electronics Engineers (IEEE) ; 2018
    In:  IEEE Transactions on Visualization and Computer Graphics Vol. 24, No. 11 ( 2018-11), p. 2906-2916
    In: IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers (IEEE), Vol. 24, No. 11 ( 2018-11), p. 2906-2916
    Type of Medium: Online Resource
    ISSN: 1077-2626 , 1941-0506 , 2160-9306
    RVK:
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2018
    detail.hit.zdb_id: 2027333-2
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  • 7
    Online Resource
    Online Resource
    Institute of Electrical and Electronics Engineers (IEEE) ; 2019
    In:  IEEE Transactions on Visualization and Computer Graphics Vol. 25, No. 5 ( 2019-5), p. 1928-1939
    In: IEEE Transactions on Visualization and Computer Graphics, Institute of Electrical and Electronics Engineers (IEEE), Vol. 25, No. 5 ( 2019-5), p. 1928-1939
    Type of Medium: Online Resource
    ISSN: 1077-2626 , 1941-0506 , 2160-9306
    RVK:
    Language: Unknown
    Publisher: Institute of Electrical and Electronics Engineers (IEEE)
    Publication Date: 2019
    detail.hit.zdb_id: 2027333-2
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  • 8
    In: JMIR Research Protocols, JMIR Publications Inc., Vol. 11, No. 8 ( 2022-8-24), p. e40445-
    Abstract: Preventable surgical errors of varying degrees of physical, emotional, and financial harm account for a significant number of adverse events. These errors are frequently tied to systemic problems within a health care system, including the absence of necessary policies/procedures, obstructive cultural hierarchy, and communication breakdown between staff. We developed an innovative, theory-based virtual reality (VR) training to promote understanding and sensemaking toward the holistic view of the culture of patient safety and high reliability. Objective We aim to assess the effect of VR training on health care workers’ (HCWs’) understanding of contributing factors to patient safety events, sensemaking of patient safety culture, and high reliability organization principles in the laboratory environment. Further, we aim to assess the effect of VR training on patient safety culture, TeamSTEPPS behavior scores, and reporting of patient safety events in the surgery department of an academic medical center in the clinical environment. Methods This mixed methods study uses a pre-VR versus post-VR training study design involving attending faculty, residents, nurses, technicians of the department of surgery, and frontline HCWs in the operation rooms at an academic medical center. HCWs’ understanding of contributing factors to patient safety events will be assessed using a scale based on the Human Factors Analysis and Classification System. We will use the data frame theory framework, supported by a semistructured interview guide to capture the sensemaking process of patient safety culture and principles of high reliability organizations. Changes in the culture of patient safety will be quantified using the Agency for Healthcare Research and Quality surveys on patient safety culture. TeamSTEPPS behavior scores based on observation will be measured using the Teamwork Evaluation of Non-Technical Skills tool. Patient safety events reported in the voluntary institutional reporting system will be compared before the training versus those after the training. We will compare the Agency for Healthcare Research and Quality patient safety culture scores and patient safety events reporting before the training versus those after the training by using descriptive statistics and a within-subject 2-tailed, 2-sample t test with the significance level set at .05. Results Ethics approval was obtained in May 2021 from the institutional review board of the University of North Carolina at Chapel Hill (22-1150). The enrollment of participants for this study will start in fall 2022 and is expected to be completed by early spring 2023. The data analysis is expected to be completed by July 2023. Conclusions Our findings will help assess the effectiveness of VR training in improving HCWs’ understanding of contributing factors of patient safety events, sensemaking of patient safety culture, and principles and behaviors of high reliability organizations. These findings will contribute to developing VR training to improve patient safety culture in other specialties.
    Type of Medium: Online Resource
    ISSN: 1929-0748
    Language: English
    Publisher: JMIR Publications Inc.
    Publication Date: 2022
    detail.hit.zdb_id: 2719222-2
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  • 9
    Online Resource
    Online Resource
    Wiley ; 2018
    In:  SID Symposium Digest of Technical Papers Vol. 49, No. 1 ( 2018-05), p. 92-95
    In: SID Symposium Digest of Technical Papers, Wiley, Vol. 49, No. 1 ( 2018-05), p. 92-95
    Abstract: Growing evidence in recent literature suggests gaze contingent varifocal Near Eye Displays (NEDs) are mitigating visual discomfort caused by the vergence‐accommodation conflict (VAC). Such displays promise improved task performance in Virtual Reality (VR) and Augmented Reality (AR) applications and demand less compute and power than light field and holographic display alternatives. In the context of this paper, we further extend the evaluation of our gaze contingent wide field of view varifocal AR NED layout [1] by evaluating optical characteristics of resolution, brightness, and eye‐box. Our most recent prototype dramatically reduces form‐factor, while improving maximum depth switching time to under 200 ms.
    Type of Medium: Online Resource
    ISSN: 0097-966X , 2168-0159
    URL: Issue
    Language: English
    Publisher: Wiley
    Publication Date: 2018
    detail.hit.zdb_id: 2526337-7
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  • 10
    Online Resource
    Online Resource
    Optica Publishing Group ; 2020
    In:  Optics Express Vol. 28, No. 18 ( 2020-08-31), p. 26636-
    In: Optics Express, Optica Publishing Group, Vol. 28, No. 18 ( 2020-08-31), p. 26636-
    Abstract: The goal of computer-generated holography (CGH) is to synthesize custom illumination patterns by modulating a coherent light beam. CGH algorithms typically rely on iterative optimization with a built-in trade-off between computation speed and hologram accuracy that limits performance in advanced applications such as optogenetic photostimulation. We introduce a non-iterative algorithm, DeepCGH, that relies on a convolutional neural network with unsupervised learning to compute accurate holograms with fixed computational complexity. Simulations show that our method generates holograms orders of magnitude faster and with up to 41% greater accuracy than alternate CGH techniques. Experiments in a holographic multiphoton microscope show that DeepCGH substantially enhances two-photon absorption and improves performance in photostimulation tasks without requiring additional laser power.
    Type of Medium: Online Resource
    ISSN: 1094-4087
    Language: English
    Publisher: Optica Publishing Group
    Publication Date: 2020
    detail.hit.zdb_id: 1491859-6
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