Eye-Tracker Data Filtering using Pulse Coupled Neural Network

S. Chartier and P. Renaud (Canada)


Eyetracker, filter, pulse couple neural network, median, noise.


Data obtained from eye-tracker are contaminated with noise due to eye blink and hardware failure to detect corneal reflection. One solution is to use a nonlinear filter such as the median. However, median filters modify both noisy and noise free data and they are therefore difficult to use in real time applications. To overcome these limits, a simplified pulse coupled neural network (PCNN) is proposed to correctly detect and remove noisy data while leaving uncorrupted data untouched. Results indicated that a filter based on a PCNN achieved a much better performance than the median filter in peak signal-to-noise ratio (PSNR) and in visual inspection.

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