Automatic Determination of Text Readability over Textured Backgrounds for Augmented Reality Systems

This paper describes a pattern recognition approach to determine readability of text labels in augmented reality systems. In many augmented reality applications, one of the ways in which information is presented to the user is to place a text label over the area of interest. However, if this informa...

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Bibliographic Details
Published in:Proceedings of the 3rd IEEE/ACM International Symposium on Mixed and Augmented Reality pp. 224 - 230
Main Authors: Leykin, Alex, Tuceryan, Mihran
Format: Conference Proceeding
Language:English
Published: Washington, DC, USA IEEE Computer Society 02.11.2004
IEEE
Series:ACM Other Conferences
Subjects:
Application software
Augmented reality
Computer science
Computing methodologies
Computing methodologies > Machine learning
Computing methodologies > Machine learning > Learning paradigms
Computing methodologies > Machine learning > Learning paradigms > Supervised learning
Computing methodologies > Machine learning > Learning paradigms > Supervised learning > Supervised learning by classification
Computing methodologies > Machine learning > Machine learning algorithms
Computing methodologies > Machine learning > Machine learning algorithms > Feature selection
Computing methodologies > Machine learning > Machine learning approaches
Computing methodologies > Machine learning > Machine learning approaches > Classification and regression trees
Graphics
Humans
Information science
Interference
Labeling
Layout
Pattern recognition
ISBN:0769521916, 9780769521916
Online Access:Get full text
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Summary:This paper describes a pattern recognition approach to determine readability of text labels in augmented reality systems. In many augmented reality applications, one of the ways in which information is presented to the user is to place a text label over the area of interest. However, if this information is placed over very busy and textured backgrounds, this can affect the readability of the text. The goal of this work was to identify methods of quantitatively describing conditions under which such text would be readable or unreadable. We used texture properties and other visual features to determine if a text placed on a particular background would be readable or not. Based on these features, a supervised classifier was built that was trained using data collected from human subjects' judgment of text readability. Using a rather small training set of about 400 human evaluations over 50 heterogeneous textures the system is able to achieve a correct classification rate of over 85%.
ISBN:0769521916
9780769521916
DOI:10.1109/ISMAR.2004.22