Robot vision
An introduction to ray tracing
An introduction to ray tracing
VRST '97 Proceedings of the ACM symposium on Virtual reality software and technology
Identifying fixations and saccades in eye-tracking protocols
ETRA '00 Proceedings of the 2000 symposium on Eye tracking research & applications
Binocular eye tracking in virtual reality for inspection training
ETRA '00 Proceedings of the 2000 symposium on Eye tracking research & applications
Binocular eye tracking in VR for visual inspection training
VRST '01 Proceedings of the ACM symposium on Virtual reality software and technology
Measuring Presence in Virtual Environments: A Presence Questionnaire
Presence: Teleoperators and Virtual Environments
Use of eye movements as feedforward training for a synthetic aircraft inspection task
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
3D point-of-regard, position and head orientation from a portable monocular video-based eye tracker
Proceedings of the 2008 symposium on Eye tracking research & applications
ACM Transactions on Applied Perception (TAP)
3D attentional maps: aggregated gaze visualizations in three-dimensional virtual environments
Proceedings of the International Conference on Advanced Visual Interfaces
Gaze-Contingent soft tissue deformation tracking for minimally invasive robotic surgery
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Proceedings of the Symposium on Eye Tracking Research and Applications
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This paper presents an improved 3D eye movement analysis algorithm for binocular eye tracking within Virtual Reality for visual inspection training. The user's gaze direction, head position and orientation are tracked to allow recording of the user's fixations within the environment. The paper summarizes methods for (1) integrating the eye tracker into a Virtual Reality framework, (2) calculating the user's 3D gaze vector, and (3) calibrating the software to estimate the user's inter-pupillary distance post-facto. New techniques are presented for eye movement analysis in 3D for improved signal noise suppression. The paper describes (1) the use of Finite Impulse Response (FIR) filters for eye movement analysis, (2) the utility of adaptive thresholding and fixation grouping, and (3) a heuristic method to recover lost eye movement data due to miscalibration. While the linear signal analysis approach is itself not new, its application to eye movement analysis in three dimensions advances traditional 2D approaches since it takes into account the 6 degrees of freedom of head movements and is resolution independent. Results indicate improved noise suppression over our previous signal analysis approach.