skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.


Title: Brightness matching in optical see-through augmented reality
A visual experiment using a beam-splitter-based optical see-through augmented reality (OST-AR) setup tested the effect of the size and alignment of AR overlays with a brightness-matching task using physical cubes. Results indicate that more luminance is required when AR overlays are oversized with respect to the cubes, showing that observers discount the AR overlay to a greater extent when it is more obviously a transparent layer. This is not explained by conventional color appearance modeling but supports an AR-specific model based on foreground-background discounting. The findings and model will help determine parameters for creating convincing AR manipulation of real-world objects.  more » « less
Award ID(s):
1942755
PAR ID:
10202194
Author(s) / Creator(s):
Publisher / Repository:
Optical Society of America
Date Published:
Journal Name:
Journal of the Optical Society of America A
Volume:
37
Issue:
12
ISSN:
1084-7529; JOAOD6
Format(s):
Medium: X Size: Article No. 1927
Size(s):
Article No. 1927
Sponsoring Org:
National Science Foundation
More Like this
  1. Chinn, C.; Tan, E.; Chan, C.; Kali, Y. (Ed.)
    Immersive AR technologies can support students’ learning processes and deep engagement with outdoor science pursuits, yet few studies explore these technologies with out-of-school learners. We analyze how immersive AR features built into an outdoor-based mobile app shaped nine families’ learning experiences as they explored pollinator habitats. Preliminary findings revealed that immersive AR scanning tools built into the Pollinator Explorers app guided families’ observational practices of real-world objects through virtual overlays representing pollinator habitats. 
    more » « less
  2. Science museums aim to engage a large, diverse public audience in science learning and consequently, attempt to present information in entertaining, socially oriented, and innovative ways. Recent work using augmented reality (defined as technology that overlays virtual objects on to the real world) engages the public using content that is both situated in the context of the exhibit and virtually generated in a way that allows hidden worlds to become visible. However, little is known about how AR technology can facilitate museum visitors science learning. The Tar AR project, a sustained collaborative partnership funded by NSF AISL with La Brea Tar Pits/Natural History Museum of Los Angeles and a local university, explores how an AR experience can: promote visitor enjoyment, enjoyment, increase understanding of scientific topics, and promote user s feelings of ease with AR technology. 
    more » « less
  3. Mobile Augmented Reality (AR), which overlays digital information with real-world scenes surrounding a user, provides an enhanced mode of interaction with the ambient world. Contextual AR applications rely on image recognition to identify objects in the view of the mobile device. In practice, due to image distortions and device resource constraints, achieving high performance image recognition for AR is challenging. Recent advances in edge computing offer opportunities for designing collaborative image recognition frameworks for AR. In this demonstration, we present CollabAR, an edge-assisted collaborative image recognition framework. CollabAR allows AR devices that are facing the same scene to collaborate on the recognition task. Demo participants develop an intuition for different image distortions and their impact on image recognition accuracy. We showcase how heterogeneous images taken by different users can be aggregated to improve recognition accuracy and provide a better user experience in AR. 
    more » « less
  4. Mobile Augmented Reality (AR), which overlays digital content on the real-world scenes surrounding a user, is bringing immersive interactive experiences where the real and virtual worlds are tightly coupled. To enable seamless and precise AR experiences, an image recognition system that can accurately recognize the object in the camera view with low system latency is required. However, due to the pervasiveness and severity of image distortions, an effective and robust image recognition solution for “in the wild” mobile AR is still elusive. In this article, we present CollabAR, an edge-assisted system that provides distortion-tolerant image recognition for mobile AR with imperceptible system latency. CollabAR incorporates both distortion-tolerant and collaborative image recognition modules in its design. The former enables distortion-adaptive image recognition to improve the robustness against image distortions, while the latter exploits the spatial-temporal correlation among mobile AR users to improve recognition accuracy. Moreover, as it is difficult to collect a large-scale image distortion dataset, we propose a Cycle-Consistent Generative Adversarial Network-based data augmentation method to synthesize realistic image distortion. Our evaluation demonstrates that CollabAR achieves over 85% recognition accuracy for “in the wild” images with severe distortions, while reducing the end-to-end system latency to as low as 18.2 ms. 
    more » « less
  5. This article discusses novel research methods used to examine how Augmented Reality (AR) can be utilized to present “omic” (i.e., genomes, microbiomes, pathogens, allergens) information to non-expert users. While existing research shows the potential of AR as a tool for personal health, methodological challenges pose a barrier to the ways in which AR research can be conducted. There is a growing need for new evaluation methods for AR systems, especially as remote testing becomes increasingly popular. In this article, we present two AR studies adapted for remote research environments in the context of personal health. The first study ( n = 355) is a non-moderated remote study conducted using an AR web application to explore the effect of layering abstracted pathogens and mitigative behaviors on a user, on perceived risk perceptions, negative affect, and behavioral intentions. This study introduces methods that address participant precursor requirements, diversity of platforms for delivering the AR intervention, unsupervised setups, and verification of participation as instructed. The second study ( n = 9) presents the design and moderated remote evaluation of a technology probe, a prototype of a novel AR tool that overlays simulated timely and actionable environmental omic data in participants' living environment, which helps users to contextualize and make sense of the data. Overall, the two studies contribute to the understanding of investigating AR as a tool for health behavior and interventions for remote, at-home, empirical studies. 
    more » « less