Tilke Judd

Learning to predict where people look

Friday 23rd of July 2010 at 12:00pm
508-20 Evans Hall

Please note that this seminar is on Friday at 12pm.

For many applications in graphics, design, and human computer interaction, it is essential to understand where humans look in a scene. Towards this goal I will present two different projects. The first tries to understand and model where people look on high resolution images using a data driven, machine learning approach. We collected eye tracking data of 15 viewers on 1003 images and use this database as training and testing to learn a model of saliency based on both bottom up and top down image features. The second project aims to understand where people look when an image is shown at lower resolutions. It uncovers trends answering the questions: how much image resolution is needed before fixations are consistent with fixations on high resolution images? at which resolution are viewers most consistent about where they look? The work suggests that viewers' fixations start to be very consistent at around the same time as viewers start to understand the image content--and that this can happen at very low resolution.

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