Using Depth Cues for Enhancing Data Visualization

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In this project, we successfully constructed mathematical models for describing the influence of perspective depth cues on our visual attention to the given visualization images. The obtained results reveal that perspective images can be still be visually consistent even when the associated distortion of the depth cues stays within in some tolerance. We can further present an algorithm for intentionally directing visual attention from viewers by introducing excessive distortion in the layout of the depth cues in the visualization images. Our new findings will serve as the basis for generating high-quality visualization images that take into account the properties inherent in our human visual system.