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Appearance is a multidimensional aspect of self-presentation that encompasses observable attributes such as clothing, grooming, speech, and nonverbal behavior. These elements are often strategically managed to align with socially constructed expectations in different settings. For instance, individuals tailor their appearance during job interviews, social gatherings, or athletic events to meet the perceived norms of those environments.Contextual Adaptation and Social SignalsThe research...
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Estimating Visibility of Annotations for View Management in Spatial Augmented Reality Based on Machine-Learning

Keita Ichihashi1, Kaori Fujinami2

  • 1Department of Computer and Information Sciences, Tokyo University of Agriculture and Technology, Tokyo 184-8588, Japan. ichihashi0402@gmail.com.

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Summary

This study introduces VisLP, a novel view management method for spatial Augmented Reality (AR). VisLP uses machine learning to optimize label and linkage line visibility for projected information, enhancing user experience in AR systems.

Keywords:
annotationaugmented realitydepth sensingfeature selectionmachine-learningmediated realitymodified perceptionprojectorspatial augmented realityview management

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Virtual Reality

Background:

  • Augmented Reality (AR) enhances perception by overlaying virtual information onto the real world.
  • Effective view management is crucial for AR information visibility, yet under-explored in spatial AR.
  • Projector-based spatial AR faces unique visibility challenges due to optical properties.

Purpose of the Study:

  • To propose VisLP, a view management method for spatial AR.
  • To enhance the visibility of labels and linkage lines in projector-based AR.
  • To leverage machine learning for accurate visibility estimation.

Main Methods:

  • Developed VisLP, a spatial AR view management technique.
  • Employed machine learning to estimate label and linkage line visibility.
  • Designed 88 features for labels and 28 for linkage lines, optimizing with subsets for speed.

Main Results:

  • VisLP achieved 76.0% agreement with user evaluations for label visibility.
  • VisLP achieved 73% agreement for linkage line visibility estimations.
  • Feature selection improved processing speed by 170% with minimal performance loss.

Conclusions:

  • VisLP effectively estimates visibility for labels and linkage lines in spatial AR.
  • The machine learning approach improves visibility and user experience in projector-based AR.
  • Optimized feature sets enhance computational efficiency for real-time AR applications.