Collins, ChristopherWang, Feiyang2021-10-152022-03-292021-10-152022-03-292021-08-01https://hdl.handle.net/10155/1372This thesis presents a novel marker-free method for identifying screens of interest when using head-mounted eye-tracking for visualization in cluttered and multi-screen environments. The presented approach offers a solution for discerning visualization entities from sparse backgrounds by incorporating edge-detection into the existing pipeline. The system allows for both more efficient screen identification and improved accuracy over the state-of-the-art ORB algorithm. To make use of this pipeline in visualization applications, a model is introduced to track a user’s interest in rendered visualization objects by collecting the gaze data and calculating the object group’s interest scores across selected visual features. With the interest model, We offer an implicit gaze interaction system that provides subtle interaction supports to improve group-of-interest objects visibility and to ease object selection in crowded regions of information visualizations.enGaze estimationFeature detectionAttention modellingImplicit interactionOverplotting visualizationThesis