Seyed Navid Haji Mirza

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This report explores the possible solutions for image annotation and retrieval by implicitly monitoring user attention via eye-tracking. Features are extracted from the gaze trajectory of users examining sets of images to provide implicit information on the target template that guides visual attention. Our Gaze Inference System (GIS) is a fuzzy logic based(More)
An innovative semi-automatic image annotation system is enriched with the feedbacks of user's eyes. This system implicitly exploits the competence of human mind and it utilizes the computational power of the computers in order to achieve a pervasive and accurate annotation. This method requires minimal user interaction. It makes it suitable to be used in(More)
This report tries to measure users’ interest in images that appear on the screen by monitoring their attention via eye-tracking. Our Gaze Inference System analyzes the gaze-movement features to assign a user interest level (UIL) from 0 to 1 to every image that appears on the screen. Because the properties of the gaze features for every user are different(More)
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