[ Back to EurekAlert! ] Public release date: 16-Jun-2010
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Contact: Katrina Norfleet
knorfleet@arvo.org
220-221-2924
Association for Research in Vision and Ophthalmology

Researchers predict human visual attention using computer intelligence for the first time

In a computerized game of 'spot the difference,' people are more likely to notice additions, removals than color changes

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 color change to be a lot easier to spot, since color plays such an important role in our day-to-day lives and visual perception," said lead researcher Milan Verma of Queen Mary.

The authors suggest that the computer-based approach will be useful in designing displays of an essential nature such as road signs, emergency services, security and surveillance to draw attention to a change or part of the display that requires immediate attention.

"We live in a world in which we are immersed in visual information," explained Verma. "The result is a huge cognitive burden which may hinder our ability to complete a given task. This study is an important step toward understanding how visual information is processed and how we can go about optimizing the presentation of visual displays."

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The Association for Research in Vision and Ophthalmology (ARVO) is the largest eye and vision research organization in the world. Members include some 12,500 eye and vision researchers from over 80 countries. The Association encourages and assists research, training, publication and dissemination of knowledge in vision and ophthalmology. For more information, visit www.arvo.org.

ARVO's Journal of Vision (www.journalofvision.org) is an online-only, peer-reviewed, open-access publication devoted to visual function in humans and animals. It explores topics such as spatial vision, perception, low vision, color vision and more, spanning the fields of neuroscience, psychology and psychophysics. JOV is known for hands-on datasets and models that users can manipulate online.

Queen Mary, University of London is one of the UK's leading research-focused higher education institutions with some 15,000 undergraduate and postgraduate students. Amongst the largest of the colleges of the University of London, Queen Mary's 3,000 staff deliver world class degree programmes and research across 21 academic departments and institutes, within three sectors: Science and Engineering; Humanities, Social Sciences and Laws; and the School of Medicine and Dentistry.

Ranked 11th in the UK according to the Guardian analysis of the 2008 Research Assessment Exercise, Queen Mary has been described as 'the biggest star among the research-intensive institutions' by the Times Higher Education and also won the 'Most Improved Student Experience' award for 2009, reflecting the superb academic and social experience offered to all students at the College. The College has a strong international reputation, with around 20 per cent of students coming from over 100 countries.

Queen Mary has an annual turnover of 220 million, research income worth 61 million, and generates employment and output worth 600 million to the UK economy each year. As a member of the 1994 Group of research-focused universities, Queen Mary has made a strategic commitment to the highest quality of research, but also to the best possible educational, cultural and social experience for its students.

The College is unique amongst London's universities in being able to offer a completely integrated residential campus, with a 2,000-bed award-winning Student Village on its Mile End campus.

Media Contact: Simon Levey
+44 (0)20 7882 5404
s.levey@qmul.ac.uk
Website: http://www.qmul.ac.uk
Twitter: http://twitter.com/QMUL



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