Semantic-aware label placement for augmented reality in street view

@article{Jia2020SemanticawareLP,
  title={Semantic-aware label placement for augmented reality in street view},
  author={Jianqing Jia and Semir Elezovikj and Heng Fan and Shuojin Yang and Jing Liu and Wei Guo and Chiu Chiang Tan and Haibin Ling},
  journal={The Visual Computer},
  year={2020},
  pages={1 - 15}
}
In an augmented reality (AR) application, placing labels in a manner that is clear and readable without occluding the critical information from the real world can be a challenging problem. This paper introduces a label placement technique for AR used in street view scenarios. We propose a semantic-aware task-specific label placement method by identifying potentially important image regions through a novel feature map, which we refer to as guidance map. Given an input image, its saliency… Expand
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References

SHOWING 1-10 OF 41 REFERENCES
Where to Place: A Real-Time Visual Saliency Based Label Placement for Augmented Reality Applications
TLDR
A novel method for optimal placement of labels for AR by an objective function that minimizes both occlusion with visually salient regions in scenes of interest, and the temporal jitter for facilitating coherence in real-time AR applications is presented. Expand
Image-Based Label Placement for Augmented Reality Browsers
TLDR
This paper proposes an image-based label placement method, which combines identifying potentially important image regions and determining manual placement tendencies while labeling, and adds geometric constraints for placing labels in the optimization problem to obtain the final label layout. Expand
Image-driven view management for augmented reality browsers
TLDR
An image-based approach, which combines a visual saliency algorithm with edge analysis to identify potentially important image regions and geometric constraints for placing labels in Augmented Reality systems, is introduced. Expand
Hedgehog labeling: View management techniques for external labels in 3D space
TLDR
This work uses 3D geometric constraints to achieve label placement that fulfills the desired objectives, but also behaves consistently over time as the viewpoint changes, and overcomes the lack of temporal coherence. Expand
Real-Time Video Annotations for Augmented Reality
TLDR
This work presents a new approach to determine and track areas with less visual interest based on feature density and to automatically compute label layout from this information, which works in under 5ms per frame and provides flexible constraints for controlling label placement behaviour to the application designer. Expand
Estimating Visibility of Annotations for View Management in Spatial Augmented Reality Based on Machine-Learning Techniques
TLDR
A view management method for spatial AR, VisLP, that places labels and linkage lines based on the estimation of the visibility that reflects human’s subjective mental workload in reading information and objective measures of reading correctness in various projection conditions is proposed. Expand
Evaluating label placement for augmented reality view management
  • Ronald T. Azuma, C. Furmanski
  • Computer Science
  • The Second IEEE and ACM International Symposium on Mixed and Augmented Reality, 2003. Proceedings.
  • 2003
TLDR
Moves of objective readability from the user study demonstrated that in practice, human subjects were able to read labels fastest with the algorithms that most quickly prevented overlap, even if placement wasn't ideal. Expand
Density-based label placement
TLDR
This work introduces a versatile density-based approach to label placement that aims to put labels in uncluttered areas of an underlying 2D visualization and supports prioritized label placement, user-defined label-to-label and label- to-feature margins, obstacle-constrained labeling, and arbitrarily shaped labels. Expand
Evaluation of labelling layout method for image-driven view management in augmented reality
TLDR
An image-driven view management method to superimpose 2D labels to the objects under indoor environments such as sculpture and toy by minimizing penalty function is studied and the proposed method represents the best results in terms of avoiding occlusion and efficiency. Expand
An empirical evaluation of labelling method in augmented reality
TLDR
The quantitative measurement of clutter indicates the change induced by labels on real scene, therefore contributing the label design on view management in future. Expand
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