Usability engineering turns 10
interactions
A toolkit for strategic usability: results from workshops, panels, and surveys
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
View management for virtual and augmented reality
Proceedings of the 14th annual ACM symposium on User interface software and technology
Handbook of Usability Testing: How to Plan, Design, and Conduct Effective Tests
Handbook of Usability Testing: How to Plan, Design, and Conduct Effective Tests
Evaluating Label Placement for Augmented Reality View Management
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
MADCOW: a multimedia digital annotation system
Proceedings of the working conference on Advanced visual interfaces
Automatic Determination of Text Readability over Textured Backgrounds for Augmented Reality Systems
ISMAR '04 Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality
An Annotation Framework for a Virtual Learning Portfolio
ICALT '06 Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies
Cognitive Psychology: Connecting Mind, Research and Everyday Experience
Cognitive Psychology: Connecting Mind, Research and Everyday Experience
User-Perceived Quality of Interactive Systems
IEEE Transactions on Software Engineering
Usability Engineering for Augmented Reality: Employing User-Based Studies to Inform Design
IEEE Transactions on Visualization and Computer Graphics
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Annotation is often used for supplement real objects in Augmented Reality. Previous researches on the annotation have been focused on optimal label placement, where annotations are placed close to the objects while avoiding overlapping. However, optimally placed annotations are still hard to recognize when the number of annotations is large. Human eyes may easily perceive an object by seeing its distinctive features. In this paper, we proposed visualization methods for easily perceivable annotations using distinctive features. The proposed methods are based on studies of effects of colors, depth, style, and transparency of the annotations.