3,225 research outputs found
EyeRIS: A General-Purpose System for Eye Movement Contingent Display Control
In experimental studies of visual performance, the need often emerges to modify the stimulus according to the eye movements perfonncd by the subject. The methodology of Eye Movement-Contingent Display (EMCD) enables accurate control of the position and motion of the stimulus on the retina. EMCD procedures have been used successfully in many areas of vision science, including studies of visual attention, eye movements, and physiological characterization of neuronal response properties. Unfortunately, the difficulty of real-time programming and the unavailability of flexible and economical systems that can be easily adapted to the diversity of experimental needs and laboratory setups have prevented the widespread use of EMCD control. This paper describes EyeRIS, a general-purpose system for performing EMCD experiments on a Windows computer. Based on a digital signal processor with analog and digital interfaces, this integrated hardware and software system is responsible for sampling and processing oculomotor signals and subject responses and modifying the stimulus displayed on a CRT according to the gaze-contingent procedure specified by the experimenter. EyeRIS is designed to update the stimulus within a delay of 10 ms. To thoroughly evaluate EyeRIS' perforltlancc, this study (a) examines the response of the system in a number of EMCD procedures and computational benchmarking tests, (b) compares the accuracy of implementation of one particular EMCD procedure, retinal stabilization, to that produced by a standard tool used for this task, and (c) examines EyeRIS' performance in one of the many EMCD procedures that cannot be executed by means of any other currently available device.National Institute of Health (EY15732-01
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Degraded reality: Using VR/AR to simulate visual impairments
The effects of eye disease cannot be depicted accurately using traditional media. Consequently, public understanding of eye disease is often poor. We present a VR/AR system for simulating common visual impairments, including disability glare, spatial distortions (Metamorphopsia), the selective blurring and filling-in of information across the visual field, and color vision deficits. Unlike most existing simulators, the simulations are informed by patients' self-reported symptoms, can be quantitatively manipulated to provide custom disease profiles, and support gaze-contingent presentation (i.e., when using a VR/AR headset that contains eye-tracking technology, such as the Fove0). Such a simulator could be used as a teaching/empathy aid, or as a tool for evaluating the accessibility of new products and environments
Perceptually guided Computer-Generated Holography
Computer-Generated Holography (CGH) promises to deliver genuine, high-quality visuals at any depth. We argue that combining CGH and perceptually guided graphics can soon lead to practical holographic display systems that deliver perceptually realistic images. We propose a new CGH method called metameric varifocal holograms. Our CGH method generates images only at a user’s focus plane while displayed images are statistically correct and indistinguishable from actual targets across peripheral vision (metamers). Thus, a user observing our holograms is set to perceive a high quality visual at their gaze location. At the same time, the integrity of the image follows a statistically correct trend in the remaining peripheral parts. We demonstrate our differentiable CGH optimization pipeline on modern GPUs, and we support our findings with a display prototype. Our method will pave the way towards realistic visuals free from classical CGH problems, such as speckle noise or poor visual quality
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A Gaze-enabled Graph Visualization to Improve Graph Reading Tasks
Performing typical network tasks such as node scanning and path tracing can be difficult in large and dense graphs. To alleviate this problem we use eye-tracking as an interactive input to detect tasks that users intend to perform and then produce unobtrusive visual changes that support these tasks. First, we introduce a novel fovea based filtering that dims out edges with endpoints far removed from a user's view focus. Second, we highlight edges that are being traced at any given moment or have been the focus of recent attention. Third, we track recently viewed nodes and increase the saliency of their neighborhoods. All visual responses are unobtrusive and easily ignored to avoid unintentional distraction and to account for the imprecise and low-resolution nature of eye-tracking. We also introduce a novel gaze-correction approach that relies on knowledge about the network layout to reduce eye-tracking error. Finally, we present results from a controlled user study showing that our methods led to a statistically significant accuracy improvement in one of two network tasks and that our gaze-correction algorithm enables more accurate eye-tracking interaction
Gaze-contingent manipulation of color perception
Using real time eye tracking, gaze-contingent displays can modify their content to represent depth (e.g., through additional depth cues) or to increase rendering performance (e.g., by omitting peripheral detail). However, there has been no research to date exploring how gaze-contingent displays can be leveraged for manipulating perceived color. To address this, we conducted two experiments (color matching and sorting) that manipulated peripheral background and object colors to influence the user's color perception. Findings from our color matching experiment suggest that we can use gaze-contingent simultaneous contrast to affect color appearance and that existing color appearance models might not fully predict perceived colors with gaze-contingent presentation. Through our color sorting experiment we demonstrate how gaze-contingent adjustments can be used to enhance color discrimination. Gaze-contingent color holds the promise of expanding the perceived color gamut of existing display technology and enabling people to discriminate color with greater precision.Postprin
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