Estimating Visibility of Annotations for View Management in Spatial Augmented Reality Based on Machine-Learning Techniques

Sensors
Keita Ichihashi, Kaori Fujinami

Abstract

Augmented Reality (AR) is a class of "mediated reality" that artificially modifies the human perception by superimposing virtual objects on the real world, which is expected to supplement reality. In visual-based augmentation, text and graphics, i.e., label, are often associated with a physical object or a place to describe it. View management in AR is to maintain the visibility of the associated information and plays an important role on communicating the information. Various view management techniques have been investigated so far; however, most of them have been designed for two dimensional see-through displays, and few have been investigated for projector-based AR called spatial AR. In this article, we propose a view management method for spatial AR, VisLP, that places labels and linkage lines based on the estimation of the visibility. Since the information is directly projected on objects, the nature of optics such as reflection and refraction constrains the visibility in addition to the spatial relationship between the information, the objects, and the user. VisLP employs machine-learning techniques to estimate the visibility that reflects human's subjective mental workload in reading information and objective measures of...Continue Reading

References

Jun 8, 2013·IEEE Transactions on Visualization and Computer Graphics·Daisuke IwaiKosuke Sato

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Software Mentioned

AR
Microsoft Kinect
RandomForest
NGD
FreeTop
RandomTree
WEKA
RandomForests
OpenCV
VisLP

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