Starburst: A hybrid algorithm for video-based eye tracking combining feature-based and model-based approaches

By Dongheng Li, D. Winfield, Derrick J. Parkhurst. Computer Vision and Pattern Recognition, 2005

463 citations recorded by the source corpus.

Subfields: Ophthalmology, Radiology, Nuclear Medicine and Imaging, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/db1d7f94e91feea0a0e0b2f4563f2d05b0338732

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Ophthalmology

1. Dan Witzner Hansen
2. Qiang Ji
3. Andreas Bulling
4. Päivi Majaranta

In Radiology, Nuclear Medicine and Imaging

1. Dan Witzner Hansen
2. Qiang Ji
3. Andreas Bulling
4. Päivi Majaranta

In Human-Computer Interaction

1. Dan Witzner Hansen
2. Qiang Ji
3. Andreas Bulling
4. Päivi Majaranta

Citations in the corpus

• EYEDIAP: a database for the development and evaluation of gaze estimation algorithms from RGB and RGB-D cameras (2014, unknown): “Two main approach categories can be identified in the literature: model-based, that leverage parametric and geometric description of the gaze observations but often require high-resolution images to extract the gaze features through for instance, the iris and pupil fitting techniques [Ishikawa et al”
• In the Eye of the Beholder: A Survey of Models for Eyes and Gaze (2010, methodology): “Simple ellipse models consist of voting-based methods [79], [84], [111], [118], [142], [165] and model fitting methods [24], [47], [89].”