Texture segmentation using Gabor modulation/demodulation
Pattern Recognition Letters
Unsupervised texture segmentation using Gabor filters
Pattern Recognition
Handbook of pattern recognition & computer vision
Annotating real-world objects using augmented reality
Computer graphics
Evaluating Label Placement for Augmented Reality View Management
ISMAR '03 Proceedings of the 2nd IEEE/ACM International Symposium on Mixed and Augmented Reality
Wearable mobile augmented reality: evaluating outdoor user experience
Proceedings of the 10th International Conference on Virtual Reality Continuum and Its Applications in Industry
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This paper concerns the application of pattern classification techniques to the domain of augmented reality. 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 predict 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 judgement 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%.