Development of Handheld Augmented Reality X-Ray for K-12 Settings

Santos, Marc Ericson C.; Terawaki, Mitsuaki; Taketomi, Takafumi; Yamamoto, Go; Kato, Hirokazu

doi:10.1007/978-3-662-44447-4_11

Cited by 6 publications

(1 citation statement)

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“…In [24], we talked about our first attempt to encourage an interdisciplinary effort in introducing AR X-ray to the classroom. We explain that the X-ray visualization is good for visualizing virtual information in the context of a real object [21].…”

Section: Past Empirical Evaluationsmentioning

confidence: 99%

Exploring legibility of augmented reality X-ray

Santos

Almeida

Yamamoto

et al. 2015

Multimed Tools Appl

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Virtual objects can be visualized inside real objects using augmented reality (AR). This visualization is called AR X-ray because it gives the impression of seeing through the real object. In standard AR, virtual information is overlaid on top of the real world. To position a virtual object inside an object, AR X-ray requires partially occluding the virtual object with visually important regions of the real object. In effect, the virtual object becomes less legible compared to when it is completely unoccluded. Legibility is an important consideration for various applications of AR X-ray. In this research, we explored legibility in two implementations of AR X-ray, namely, edge-based and saliency-based. In our first experiment, we explored on the tolerable amounts of occlusion to comfortably distinguish small virtual objects. In our second experiment, we compared edge-based and saliency-based AR X-ray methods when visualizing virtual objects inside various real objects. Moreover, we benchmarked the legibility of these two methods against alpha blending. From our experiments, we observed that users have varied preferences for proper amounts of occlusion cues for both methods. The partial occlusions generated by the edgebased and saliency-based methods need to be adjusted depending on the lighting condition and the texture complexity of the occluding object. In most cases, users identify objects faster with saliency-based AR X-ray than with edge-based AR X-ray. Insights from this research can be directly applied to the development of AR X-ray applications.

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Section: Past Empirical Evaluationsmentioning

confidence: 99%