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"content": "Gaze visualization has been used to understand the results from gaze tracking studies in a wide range of fields. In the medical field, diagnoses of medical images have been studied with gaze tracking technology to understand how radiologists read medical images. While prior work were mainly based on diagnosis with a single image, recent work focused on diagnosis with consecutive cross-sectional medical images acquired from preoperative computed tomography (CT) or magnetic resonance imaging (MRI). In the diagnosis, radiologists scroll through a stack of images to get a 3D cognition of organs and lesions. Thus, it is important to understand radiologists’ gaze patterns three dimensionally across such contiguous cross-sectional images. However, little has been done to visualize more complicated gaze patterns from the contiguous cross-sectional medical images. To address this problem, we present an interactive 3D gaze visualization tool, GazeVis, where InfoVis and SciVis techniques are harmonized to show the abstract gaze data along with a realistic 3D rendering of the visual stimuli (i.e., organs and lesions). We present case studies with 12 radiologists who use GazeVis to investigate gaze patterns of their colleagues with different levels of expertise, providing empirical evidences about the competence of our gaze visualization system.",
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"abstract": "Gaze tracking is a useful human-to-computer interface, which plays an increasingly important role in a range of mobile applications. Gaze calibration is an indispensable component of gaze tracking, which transforms the eye coordinates to the screen coordinates. The existing approaches of gaze tracking either have limited accuracy or require the user’s cooperation in calibration and in turn hurt the quality of experience. We in this paper propose vGaze, continuous gaze tracking with implicit saliency-aware calibration on mobile devices. The design of vGaze stems from our insight on the temporal and spatial dependent relation between the visual saliency and the user’s gaze. vGaze is implemented as a light-weight software that identifies video frames with “useful” saliency information, sensing the user’s head movement, performs opportunistic calibration using only those “useful” frames, and leverages historical information for accelerating saliency detection. We implement vGaze on a commercial mobile device and evaluate its performance in various scenarios. The results show that vGaze can work at real time with video playback applications. The average error of gaze tracking is 1.51 cm (2.884<inline-formula><tex-math notation=\"LaTeX\">Z_${}^{\\circ }$_Z</tex-math></inline-formula>) which decreases to 0.99 cm (1.891<inline-formula><tex-math notation=\"LaTeX\">Z_${}^{\\circ }$_Z</tex-math></inline-formula>) with historical information and 0.57 cm (1.089<inline-formula><tex-math notation=\"LaTeX\">Z_${}^{\\circ }$_Z</tex-math></inline-formula>) with an indicator.",
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