International Journal of Computer Vision
Eye tracking in advanced interface design
Virtual environments and advanced interface design
Evaluation of eye gaze interaction
Proceedings of the SIGCHI conference on Human Factors in Computing Systems
Finding generalized projected clusters in high dimensional spaces
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Recognition without Correspondence using MultidimensionalReceptive Field Histograms
International Journal of Computer Vision
Design issues of iDICT: a gaze-assisted translation aid
ETRA '00 Proceedings of the 2000 symposium on Eye tracking research & applications
Identifying fixations and saccades in eye-tracking protocols
ETRA '00 Proceedings of the 2000 symposium on Eye tracking research & applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Recognize Human Action Sequences
ICDL '02 Proceedings of the 2nd International Conference on Development and Learning
Estimating focus of attention based on gaze and sound
Proceedings of the 2001 workshop on Perceptive user interfaces
A multimodal learning interface for grounding spoken language in sensory perceptions
ACM Transactions on Applied Perception (TAP)
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An intelligent human-computer interface is expected to allow computers to work with users in a cooperative manner. To achieve this goal, computers need to be aware of user attention and provide assistances without explicit user request. Cognitive studies of eye movements suggest that in accomplishing well-learned tasks, the performer's focus of attention is locked with the ongoing work and more than 90% of eye movements are closely related to the objects beingmanipulated in the tasks. In light of this, we have developed an attentional object spotting system that integrates multimodal data consisting of eye positions, head positions and video from the "first-person" perspective. To detect the user's focus of attention, we modeled eye gaze and head movements using a hidden Markov model (HMM) representation. For each attentional point in time, the object of user interest is automatically extracted and recognized. We report the results of experiments on finding attentional objects in the natural task of "making a peanut-butter sandwich".