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Researchers predict human visual attention using computer intelligence for the first time
Date:6/16/2010

Rockville, MD Scientists have just come several steps closer to understanding change blindness the well studied failure of humans to detect seemingly obvious changes to scenes around them with new research that used a computer-based model to predict what types of changes people are more likely to notice.

These findings on change blindness were presented in a Journal of Vision article, "A semi-automated approach to balancing bottom-up salience for predicting change detection performance."

"This is one of the first applications of computer intelligence to help study human visual intelligence, " said author Peter McOwan, professor at Queen Mary, University of London. "The biologically inspired mathematics we have developed and tested can have future uses in letting computer vision systems such as robots detect interesting elements in their visual environment."

During the study, participants were asked to spot the differences between pre-change and post-change versions of a series of pictures. Some of these pictures had elements added, removed or color altered, with the location of the change based on attention grabbing properties (this is the "salience" level referred to in the article).

Unlike previous research where scientists studied change blindness by manually manipulating such pictures and making decisions about what and where to make a change, the computer model used in this study eliminated any human bias. The research team at Queen Mary's School of Electronic Engineering and Computer Science developed an algorithm that let the computer "decide" how to change the images that study participants were asked to view.

While the experiments confirmed that change blindness can be predicted using this model, the tests also showed that the addition or removal of an object from the scene is detected more readily than changes in the color of the object, a result that surprised the scientists. "We expected a co
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Contact: Katrina Norfleet
knorfleet@arvo.org
220-221-2924
Association for Research in Vision and Ophthalmology
Source:Eurekalert

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