Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1145/2166966.2167012 | IUI |
Keywords | Field | DocType |
precise alignment,image registration,eye-tracking measurement error,good alignment,optimization strategy,text-gaze alignment,unconstrained task,linear transformation,multi-blur optimization,eye-tracking device,reliable use,monte carlo,local minima,measurement error,error correction,eye tracking | Computer vision,Monte Carlo method,Gaze,Computer science,Textual information,Maxima and minima,Error detection and correction,Artificial intelligence,Linear map,Observational error,Image registration | Conference |
Citations | PageRank | References |
5 | 0.49 | 4 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Pascual Martinez-Gomez | 1 | 8 | 1.60 |
| Chen Chen | 2 | 5 | 0.82 |
| Tadayoshi Hara | 3 | 118 | 9.54 |
| Yoshinobu Kano | 4 | 424 | 30.19 |
| Akiko N. Aizawa | 5 | 678 | 120.63 |