The Effect of Focal Distance, Age, and Brightness on Near-Field Augmented Reality Depth Matching
November 30, 2017 Β· Declared Dead Β· π IEEE Transactions on Visualization and Computer Graphics
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Gujot Singh, Stephen R. Ellis, J. Edward Swan
arXiv ID
1712.00088
Category
cs.HC: Human-Computer Interaction
Citations
79
Venue
IEEE Transactions on Visualization and Computer Graphics
Last Checked
5 months ago
Abstract
Many augmented reality (AR) applications operate within near-field reaching distances, and require matching the depth of a virtual object with a real object. The accuracy of this matching was measured in three experiments, which examined the effect of focal distance, age, and brightness, within distances of 33.3 to 50 cm, using a custom-built AR haploscope. Experiment I examined the effect of focal demand, at the levels of collimated (infinite focal distance), consistent with other depth cues, and at the midpoint of reaching distance. Observers were too young to exhibit age-related reductions in accommodative ability. The depth matches of collimated targets were increasingly overestimated with increasing distance, consistent targets were slightly underestimated, and midpoint targets were accurately estimated. Experiment II replicated Experiment I, with older observers. Results were similar to Experiment I. Experiment III replicated Experiment I with dimmer targets, using young observers. Results were again consistent with Experiment I, except that both consistent and midpoint targets were accurately estimated. In all cases, collimated results were explained by a model, where the collimation biases the eyes' vergence angle outwards by a constant amount. Focal demand and brightness affect near-field AR depth matching, while age-related reductions in accommodative ability have no effect.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted