A survey of image registration techniques
ACM Computing Surveys (CSUR)
User-oriented document summarization through vision-based eye-tracking
Proceedings of the 14th international conference on Intelligent user interfaces
An iterative image registration technique with an application to stereo vision
IJCAI'81 Proceedings of the 7th international joint conference on Artificial intelligence - Volume 2
The text 2.0 framework: writing web-based gaze-controlled realtime applications quickly and easily
Proceedings of the 2010 workshop on Eye gaze in intelligent human machine interaction
PRICAI'12 Proceedings of the 12th Pacific Rim international conference on Trends in Artificial Intelligence
Recognition of understanding level and language skill using measurements of reading behavior
Proceedings of the 19th international conference on Intelligent User Interfaces
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Applications using eye-tracking devices need a higher accuracy in recognition when the task reaches a certain complexity. Thus, more sophisticated methods to correct eye-tracking measurement errors are necessary to lower the penetration barrier of eye-trackers in unconstrained tasks. We propose to take advantage of the content or the structure of textual information displayed on the screen to build informed error-correction algorithms that generalize well. The idea is to use feature-based image registration techniques to perform a linear transformation of gaze coordinates to find a good alignment with text printed on the screen. In order to estimate the parameters of the linear transformation, three optimization strategies are proposed to avoid the problem of local minima, namely Monte Carlo, multi-resolution and multi-blur optimization. Experimental results show that a more precise alignment of gaze data with words on the screen can be achieved by using these methods, allowing a more reliable use of eye-trackers in complex and unconstrained tasks.